{"paper_id":"e47c7bbe-34d9-480f-b1da-0e08bb8cb88a","body_text":"Assessing the value for money, from a policy maker prospective, of 24 randomised controlled trial designs for an online weight maintenance guided self-help intervention: An expected value of sample information analysis. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Assessing the value for money, from a policy maker prospective, of 24 randomised controlled trial designs for an online weight maintenance guided self-help intervention: An expected value of sample information analysis. Penny Breeze, Katharine Pidd, Daniel Pollard, Shijie Ren, Sarah Bates, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4901753/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 May, 2025 Read the published version in International Journal of Obesity → Version 1 posted 9 You are reading this latest preprint version Abstract Objective: To analyse whether conducting a randomised controlled trial (RCT) to evaluate an online weight maintenance guided self-help intervention (the SWiM intervention) would offer good value for money in the United Kingdom. Method We examined 24 RCT designs by varying inclusion criteria (participants completing behavioural weight management, specialist-led weight management, diabetes prevention programme, type 2 diabetes remission, digital weight management, all weight management services), trial duration (1-2 years), and sample size (n=500 or 2000). Trial benefits were estimated by the method of expected value of sample information analysis using a health economic model. The model examines how the proposed intervention affects weight maintenance over time (with uncertainty), and generates estimated lifetime Quality Adjusted Life Years (QALYs) and National Health Service (NHS) costs. Structured expert elicitation with 4 experts was undertaken to quantify pre-trial uncertainty in the effectiveness of SWiM compared with usual care. All trial designs were simulated to estimate trial benefits: the reduction in the costs of an inefficient decision for future populations over 10 years. Trial designs offer value for money if trial benefits exceed trial costs. Results: For three inclusion criteria options (groups recently completing ‘diabetes remission’, ‘digital weight management’ or ‘specialist weight management’), the cost of the proposed trials was estimated to exceed the estimated trial benefit (value of the reduction in decision uncertainty) over 10 years. For the other three inclusion criteria options (groups recently completed ‘behavioural weight management’, ‘diabetes prevention programme’, or ‘all weight loss programmes’), 12 trial designs produced greater benefits than costs. The optimal trial design option would include ‘all weight loss programmes’, with 2 years follow-up and sample size n=2000. Conclusion: Investment in an RCT to evaluate the SWiM intervention with two years of follow-up patients completing a range of weight loss interventions offers the greatest value to the NHS. Health sciences/Health care/Weight management Health sciences/Health care/Health policy Health sciences/Health care/Disease prevention Figures Figure 1 Figure 2 Background Behavioural weight management programmes are a cost-effective weight loss intervention ( 1 ). Weight loss can lead to health improvements and has been shown to reduce the risk of diabetes, and improve the management of type 2 diabetes ( 2 ). As such, in the United Kingdom the National Institute for Health and Care Research (NICE) recommends multicomponent weight management services for people with overweight or obesity ( 3 ). The long-term health benefits of weight management are more likely to be sustained when weight loss is maintained ( 2 ). However, weight loss programmes often follow a pattern of substantial weight loss in the initial stages followed by some or total weight regain ( 1 , 4 ). Publicly funded services specifically designed for weight maintenance are not available to support patients in the UK. Weight management could be improved if effective weight maintenance interventions were available to support adults to maintain their weight loss and the associated health benefits. Acceptance-based behavioural interventions have superior long-term weight outcomes compared to standard behavioural programmes.( 5 ) A rigorous process was taken to develop Supporting Weight Management (SWiM), a web-based, guided self-help intervention that uses Acceptance and Commitment Therapy (ACT).( 5 ) SWiM was designed to be implemented at scale and therefore uses digital technology and non-specialist guides to reduce the resources needed to deliver an ACT-based intervention. A feasibility study evaluating the SWiM programme assessed the feasibility and effectiveness of the intervention.( 6 ) However, the effectiveness and cost-effectiveness is uncertain, and a full scale randomised controlled trial (RCT) is needed to generate estimates of intervention effectiveness that can inform policy decisions to commission a new service. Health economic models are commonly used to extrapolate short-term intervention effectiveness evidence into robust estimates of the lifetime costs, Quality Adjusted Life Years (QALYs) and incremental cost-effectiveness to inform national guidelines.( 7 ) This method provides a formal process to evaluate whether a new intervention provides value for money compared with other policy options. This decision will always be made with uncertainty and the analysis should communicate the uncertainty to policymakers. Where decisions are highly uncertain there is a risk that the analysis will make an incorrect recommendation and it is advisable to collect more data prior to implementing the new intervention at scale. Health economic modelling can also be used to inform a decision to collect more data. Value of Information analysis allows new research proposals to be valued in terms of the anticipated reduction in uncertainty in cost-effectiveness estimates used for policy decision-making. Expected value of sample information (EVSI) is a method that quantifies the reduction in uncertainty of undertaking a specific research design( 8 ). Economists can then calculate the expected net benefit of sampling (ENBS), which is the difference between the expected value of the research and the expected research costs. Additionally alternative designs can be compared to identify designs that maximise the ENBS. Taken together these approaches offer research funders a framework to improve research funding allocation decisions for quantitative research informing economic evaluations and can guide research design. In this study we estimate the expected value of an RCT to provide evidence on the effectiveness of a weight maintenance intervention. We have identified three important design options for the RCT: sample size; duration of follow-up and inclusion criteria. This study aimed to evaluate the EVSI and ENBS of twenty-four RCT designs for SWIM. Methods Overview Health economic models use probabilistic sensitivity analysis to describe the uncertainty about whether a new intervention is cost-effective compared to a comparator. The analysis may indicate that the intervention is likely to be cost-effective, but the estimate is not certain. In PSA, model parameter inputs are represented as distributions around the point estimate to capture the uncertainty in the analysis. This is used to estimate the likelihood that an intervention meets a well-established willingness to pay threshold for a cost per quality adjusted life year (QALY) gained. New research will reduce the uncertainty in model parameters and reduce the risk of a sub-optimal decision; i.e. a decision to fund an intervention based on an expected cost-effectiveness estimate when the true cost-effectiveness is above the cost-per-QALY threshold. The strength and precision of evidence generated by a new RCT will depend on the trial design. In this value of information analysis, we simulated RCT data using prior expectations about the likely effectiveness of the intervention to generate a range of potential outcomes from each of 24 potential trial designs. Trial designs were varied by sample size, duration of follow-up and population inclusion criteria. Trial designs that collect more data will increase precision in their estimates and have greater value because they reduce more uncertainty. The analysis values the reduction in uncertainty provided by the simulated RCT data in terms of the increase in the expected health and economic benefit for a single patient. This is consistent with the output from health economic analyses, but this only describes the benefit at the patient level. The decision-maker should consider the overall benefit to society. The total value to society of the RCT is conditional on how many patients will receive the intervention and over what time horizon. The Health Economic model The School for Public Health Research (SPHR) Diabetes prevention model (version 5.2) is used here to assess the cost-effectiveness of SWiM versus standard care from an NHS and Personal Social Services perspective ( 1 ). The model is an individual patient level microsimulation based on the evolution of personalised trajectories for metabolic factors, including body mass index (BMI) and HbA1c. It uses existing statistical models from the UKPDS ( 9 , 10 ), and Whitehall II ( 11 ), and their links to major health outcomes. The model simulates the risk of type 2 diabetes, or diabetes related complications depending on the individual’s characteristics and metabolic health status. Full details of the model and the sources of data for the uncertain parameters are provided in the supplementary appendix. The model can be used to assess the long-term cost-effectiveness of weight loss interventions in the UK by analysing the health impact of weight maintenance and HbA1c between an intervention and do-nothing option, and cost impact of the intervention. The lifetime discounted costs and QALYs are used to generate incremental cost-effectiveness ratios, and incremental Net Monetary Benefit assuming a willingness to pay threshold of £20,000 ( 12 ). The SWIM intervention has been developed to support patients who have recently completed a weight management programme. The baseline population entering the model uses individual-level data from the adult Health Survey for England 2018 population ( 13 ). For each RCT inclusion criteria option individuals sampled to be representative of the sociodemographic and medical history of participants completing weight management services currently recommended or commissioned in the UK. Five services that were identified to precede SWIM were behavioural weight management intervention (Tier 2 weight management ( 14 )), specialist weight management (Tier 3 weight management ( 14 )), Diabetes prevention programme (NHS Diabetes Prevention Programme( 15 )), Diabetes remission (NHS Path to remission ( 16 )), and Digital weight management (NHS Digital weight management ( 17 )). A sixth population described an ‘all weight management’ population in which participants were referred from any of the five services to receive SWIM. Iterative proportional fitting methods were used to generate populations entering the model to align with different patient populations completing these weight management programmes that could be eligible for the new intervention ( 18 ). Details of the data and methods used to describe population characteristics are detailed in the supplementary appendix. In each analysis the population entering the model were assumed to have completed a weight loss programme. The effect of the initial weight loss programme was simulated conditional on the simulated individual’s characteristics, but not assumed to impact the effectiveness of SWIM. Table 1 reports summary statistics for each eligible population evaluated in the model. Table 1 Baseline characteristics for the 50,000 simulated individuals & estimated annual eligible population in England for each of 6 defined population trial inclusion criteria options Population inclusion criteria - People who recently completed Behavioural weight management Specialist weight management Diabetes prevention programme Digital weight management Diabetes remission All weight loss programme populations Number (%) Number (%) Number (%) Number (%) Number (%) Number (%) Male 12 127 (24%) 14 984 (30%) 22 464 (45%) 16 293 (33%) 23 419 (47%) 16 942 (33.9%) Female 37 873 (76%) 35 016 (70%) 27 536 (55%) 33 707 (67%) 26 581 (53%) 33 058 (66.1%) White 43 435 43 687 (87%) 44 413 (89%) 41 810 (84%) 40 054 (80%) 43 028 (86.1%) Black African/Caribbean 1 988 2 592 (5%) 1 254 (3%) 2 591 (5%) 4 297 (9%) 2 431 (4.9%) Asian 3 178 2 880 (6%) 3 487 (7%) 4 775 (10%) 4 701 (9%) 34 77 (7.0%) Other ethnicity 1 399 841 (2%) 846 (2%) 824 (2%) 948 (2%) 1 064 (2.1%) Underweight/Healthy (< 25kg/m2) 0 0 9 120 (18%) 0 (0) 5 269 (11%) 27 43 (5.5%) Overweight (25–30) 7 721 (15%) 0 17 711 (35%) 697 (1.4%) 8 803 (18%) 8 321 (16.6%) Obesity class 1 and 2 (30–40) 30 200 (60%) 16 281 (33%) 23 169 (46%) 33 420 (66.8%) 35 928 (72%) 25 554 (51.1%) Obesity class 3 (40+) 12 079 (24%) 33 719 (67%) 0 15 883 (31.8%) 0 13 382 (26.8%) Current smoker 7 967 (16%) 7 169 (14%) 7 789 (16%) 4 589 (9%) 6 738 (13%) 7 575 (15.2%) Type 2 diabetes 3 307(7%) 16 088 (32.2%) 0 (0%) 19 089 (38.2%) 50 000 (100%) 14 541 (29.1%) Non-diabetic hyperglycaemia 5 236 (10.5%) 5 634 (11.3%) 50 000 (100%) 25 467 (10.9%) 0 (0%) 13 209 (26.4%) Normoglycemia 41 457 (82.9%) 28 278 (56.6%) 0 (0%) 5 444 (50.9%) 0 (0%) 22 250 (44.5%) Hypertension 6 215 (12%) 11 639 (23%) 13 888 (28%) 36 308 (73%) 16 458 (33%) 11 108 (22.2%) Statins 4 330 (9%) 9 157 (18%) 14 730 (29%) 16 612 (33%) 18 114 (36%) 10 135 (20.3%) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Age 50.4 (15.3) 53.4 (15.9) 64.3 (12.9) 59.5 (13.7) 60.5 (12.4) 55.7 (15.5) BMI (kg/m 2 ) 35.23 (6.44) 41.47 (5.78) 30.11 (5.87) 37.43 (6.29) 33.42 (6.68) 35.15 (7.26) HbA1c (%) 5.62 (0.72) 6.14 (1.18) 6.12 (0.13) 6.34 (1.24) 7.46 (1.40) 6.21 (1.16) SBP (mmHg) 126.36 (16.97) 129.98 (18.04) 126.81 (16.09) 131.65 (17.77) 132.62(15.87) 128.49 (16.98) CHOL (mmol/l) 5.19 (1.01) 5.00 (1.03) 5.14 (1.10) 4.97 (1.01) 4.62 (1.10) 5.04 (1.09) HDL (mmol/l) 1.40 (0.38) 1.31 (0.36) 1.37 (0.39) 1.38 (0.38) 1.30 (0.38) 1.36 (0.38) Height (m) 1.65 (0.09) 1.64 (0.10) 1.65 (0.10) 1.64 (0.10) 1.66 (0.10) 1.65 (0.10) Estimated annual eligible population in England 14 901 5 446 53 000 14 000 1 000 86 728 BMI Body Mass Index following weight management programme; HDL High-density lipoprotein; SBP systolic blood pressure; SD standard deviation Table 2 Pre-Trial Uncertainty in Health Economic outcomes based on 3000 probabilistic sensitivity analysis samples of the prior parameter values incorporating the structured elicitation from 4 experts on the uncertainty in effectiveness of SWiM intervention versus usual care Prior Mean Incremental Costs [95% credible interval] Prior Mean Incremental QALYS Prior Mean Incremental Cost per QALY (ICER) Net Monetary Benefit (QALY valued at £20,000) Probability cost-effective at £20,000 per QALY threshold Behavioural weight management £94.63 [£-232.90, £195.87] 0.019 [0.000, 0.046] £4 940.31 £287.37 [£-188.02, £1010.26] 0.76 Specialist weight management 42.14 [£-418.94, £185.74] 0.023 [0.001, 0.062] £1 852.32 £411.86 [£-171.01, £1603.16] 0.78 Diabetes Prevention Programme £105.72 [£-141.59, £177.62] 0.013 [0.000, 0.038] £7 834.89 £164.16 [£-167.37, £877.18] 0.65 Digital weight management £28.76 [£-447.58, £181.10] 0.024 [0.001, 0.080] £1 181.05 £459.23 [£-167.99, £2003.37] 0.80 Diabetes remission £9.87 [£-512.90, £172.19] 0.017 [0.000, 0.050] £579.00 £332.13 [£-159.71, £1487.70] 0.74 All patients £71.03 [£-300.54, £183.95] 0.018 [0.001, 0.045] £3 885.30 £294.97 [£-173.07, £1162.00] 0.75 Prior parameter distributions In line with terminology used in Bayesian statistics, we refer to parameters that will be updated with trial data as prior parameter distributions. The prior parameter distributions were specified in line with current beliefs on the effectiveness of the intervention. The SWIM feasibility study provided an estimate of the mean difference in weight between the SWiM intervention and usual care after 6 months ( 19 ). This data is from a small feasibility study and the effectiveness beyond 6 months is unknown so we consulted with stakeholders with expertise in weight management to characterise prior parameters for the weight difference over time. We employed the Sheffield ELicitation Framework (SHELF) methodology to elicit expert opinions on the distribution of uncertainty in intervention effects on weight( 20 ), and followed best practice guidance( 21 ). We circulated an evidence dossier to workshop participants based on the SWIM feasibility study (6 month weight outcome) with evidence synthesis of similar interventions (ACT and behavioural weight maintenance interventions) to prior to the workshop. The evidence dossier identified that behavioural weight maintenance interventions reduce weight regain, and there is some evidence that ACT-based interventions are more effective than standard behavioural approaches. The workshop aimed to estimate probability distributions for the difference in weight at 12 and 24 months for SWiM compared with usual care ( 20 ). The difference in weight at 24 months was elicited as conditional on weight difference at 12 months, such that larger treatment effects at 12 months are associated with larger treatment effects at 24 months. The outcomes of the expert elicitation are reported in Section 11 of the supplementary appendix. The final prior parameter distributions for weight are illustrated in Fig. 1 . We assumed that the effects of SWiM on change in HbA1c would be conditional on weight loss. We used associations between these outcomes derived from statistical analyses of two weight management randomised controlled trials. The association was conditional on diabetes status and alternative equations were used for individuals with non-hyperglycaemic, non-diabetic hyperglycaemia and type 2 diabetes (Supplementary Information: Section 11). The intervention cost for SWIM was estimated to be £298 and no cost was assigned to usual care. The breakdown of costs associated with the intervention are reported in Section 12 of the supplementary appendix. Simulating the proposed RCT designs The analysis considered twenty-four trial design options combining two sample size options (500 participants, 2000 participants), two duration of follow-up options (12 months, 24 months) and six inclusion criteria options (Table 3 ). For the inclusion criteria we estimated the EVSI for populations completing five behavioural and weight management programmes representative of the broad range of behavioural programmes available and illustrated by those currently funded in the UK. We also evaluated the EVSI for a trial combining a population from all five programmes (Table 1 ). Table 3 Results from simulation of 3000 possible future trial outcomes: Simulated mean difference for SWiM versus Usual Care in Weight and HbA1c across 24 proposed trial designs Population inclusion criteria People who recently completed … Duration of follow-up Sample size 12 Months 24 Months Mean Change in Weight (kg) (standard error) Mean Change in Hba1c (%) (standard error Mean Change in Weight (kg) (standard error) Mean Change in Hba1c (units) (standard error Behavioural weight management 1 year 500 -1.953 (0.034) -0.079 (0.006) 2000 -1.957 (0.033) -0.088 (0.003) 2 years 500 -1.953 (0.034) -0.079 (0.006) -1.619 (0.033) -0.069 (0.006) 2000 -1.957 (0.033) -0.088 (0.003) -1.606 (0.033) -0.065 (0.003) Specialist weight management 1 year 500 -1.952 (0.033) -0.132 (0.006) 2000 -1.953 (0.033) -0.127 (0.004) 2 years 500 -1.952 (0.033) -0.132 (0.006) -1.606 (0.033) -0.109 (0.006) 2000 -1.953 (0.033) -0.127 (0.004) -1.604 (0.033) -0.105 (0.004) Diabetes prevention programme 1 year 500 -1.953 (0.034) -0.102 (0.006) 2000 -1.952 (0.033) -0.106 (0.003) 2 years 500 -1.953 (0.034) -0.102 (0.006) -1.610 (0.033) -0.095 (0.006) 2000 -1.952 (0.033) -0.106 (0.003) -1.607 (0.033) -0.083 (0.003) Digital weight management 1 year 500 -1.953 (0.033) -0.147 (0.006) 2000 -1.950 (0.033) -0.143 (0.004) 2 years 500 -1.953 (0.033) -0.147 (0.006) -1.603 (0.033) -0.117 (0.006) 2000 -1.950 (0.033) -0.143 (0.004) -1.605 (0.033) -0.117 (0.004) Diabetes remission 1 year 500 -1.954 (0.034) -0.277 (0.008) 2000 -1.947 (0.033) -0.272 (0.006) 2 years 500 -1.954 (0.034) -0.277 (0.008) -1.604 (0.033) -0.217 (0.007) 2000 -1.947 (0.033) -0.272 (0.006) -1.606 (0.033) -0.217 (0.005) All weight loss programme populations 1 year 500 -1.960 (0.034) -0.131 (0.006) 2000 -1.950 (0.033) -0.135 (0.004) 2 years 500 -1.960 (0.034) -0.131 (0.006) -1.612 (0.033) -0.104 (0.006) 2000 -1.950 (0.033) -0.135 (0.004) -1.604 (0.033) -0.108 (0.004) Standard deviations used in sampling are calculated based on the sample size, with an assumed total participant loss to follow-up of 30% per trial independent of trial design. The difference in weight and HbA1c over time for SWIM compared with usual care were assumed to be observable in an RCT to provide data on up to 4 quantities ( 1 ) difference in weight at 12 months, ( 2 ) difference in HbA1c at 12 months, ( 3 ) difference in weight at 24 months, and ( 4 ) difference in HbA1c at 24 months. We simulated RCT data to predict the four observable quantities from an RCT using Microsoft Excel (Version 3202). The mean change in weight at 12 months and 24 months were sampled from the prior parameter distribution. Variability in the weight was assumed with a standard deviation of 6.4 to be consistent with data from a previous weight management trial ( 1 ). The sampled prior information for the relationships between weight and HbA1c by diabetes status was used to estimate the mean change in HbA1c for the population. The standard deviation for HbA1c was 5.95 based on a previous weight loss trial ( 1 ). For all study designs we assume that 30% of participants would be lost to follow-up ( 22 ) The EVSI and ENBS analysis We used an efficient regression-based approximation method, that has been previously tested and evaluated, to generate EVSI estimates ( 23 ). Approximation methods are less computationally demanding processes that are compatible with more complex simulation approaches ( 24 ). The method required the following steps: We performed a Probabilistic Analysis of the SPHR diabetes prevention model simulation to obtain a sample of 3000 uncertain parameter inputs and corresponding discounted costs, QALYs. The prior incremental Net Monetary Benefit for each programme population based on the prior parameter distributions, was calculated from the simulated costs and QALYs for each PSA parameter input sample. We simulated summary trial outcomes for weight and HbA1c differences for all the proposed trials. For each trial design 3000 trial outcomes were sampled from a normal distribution in which each the mean was equal to prior parameter input sample and estimated standard error based on the trial sample size and standard deviation. We used the Sheffield Accelerated Value of Information tool (SAVI) ( 25 ) to apply the efficient regression based approach to calculate per person EVSI. This describes the extent to which the additional information from the study reduces the probability, and expected losses, of an inefficient decision. We extracted the values from SAVI to estimate the per-patient EVSI for the SWiM intervention. The population Expected Value of Sample Information was calculated for the potential eligible population over the time horizon the intervention will be implemented. We estimate the potential eligible population as the estimated annual size of the population completing the weight management service. The per person ESVI was multiplied by the total population for a time horizon of 10 years. 5. The Expected Net Benefit of Sampling is calculated by subtracting the estimated cost of the RCT away from the population Expected Value of Sample Information. For each population sample we have run a PSA sample with 3000 sampled inputs for a population of 50,000 individuals to generate 3000 Incremental Net Monetary Benefit estimates based on prior parameter distributions with confidence intervals. The stability of the model across individuals and PSA runs are reported in Section 13 of the supplementary appendix.. The EVSI calculation generates a value of information per person who will be affected by the decision. To make this useful for publicly funded research funders in the UK, we multiply the per person EVSIs with the potential eligible population. This estimates the total value of research from an NHS and personal social services perspective. We assume that the decision could benefit patients for 10 years to be consistent with other EVSI studies, but also to acknowledge observations that the intervention will be superseded by other policies in the long term. The number of people who benefit in each of the five programme populations listed above have been estimated ( 26 – 28 ), with full details provided in the supplementary appendix. The size of the sixth all weight management population is the sum of the five individual services, adjusting for overlapping populations (Table 1 ). We estimate the per-person EVSI for a total of 24 trial designs, combining alternative combinations of sample size, duration of follow-up and inclusion criteria. For each trial design we also report the total EVSI over the eligible population and 10 year time horizon. We approximate the cost of an RCT including fixed overhead costs for research consumables, staff costs were conditional on the duration of follow-up, and per participant costs conditional on the number of participants recruited. The final costs estimate for the research designs are reported in Table 5, with full details provided in Section 15 of the supplementary appendix. Results The SWiM intervention is most likely to be cost-effective compared with usual care across all the weight management populations, however there is reasonable uncertainty given the effectiveness of the intervention has not been established in a full-scale RCT. The probability that the intervention is cost-effective at the £20,000 willingness to pay threshold is between 65–80%. The results suggest that the expected Net Monetary Benefit of the intervention varies when offered to alternative weight management populations. SWIM following a digital weight management programme offers the highest expected Net Monetary Benefit and Diabetes Prevention Programme the lowest, due to variation in average BMI and prevalence of comorbidities. The diabetes remission population estimates greater cost-savings than other populations due to the additional cost saving from averting diabetes medication costs and delayed treatment escalation. The prior incremental expected net monetary benefit of the SWIM intervention across all five populations completing alternative weight management programmes and a combined population is reported in Table 3 . The simulated difference in weight and HbA1c are reported in Table 4 for each RCT design. Increasing the sample size reduces the uncertainty in measure of weight and HbA1c, as illustrated in the standard errors reported in Table 4 . Increasing the duration of follow-up to 24 months provides in additional evidence to reduce uncertainty in the maintenance of weight and HbA1c changes. Table 4 Results Comparing Expected Trial Cost with Expected Value of Sample Information for 24 possible RCT designs in 6 population subgroup inclusion criteria over a 10 year time horizon in England Population subgroup inclusion criteria – People who have recently completed … Duration of follow-up Sample size Annual eligible population (A) Estimated RCT cost (B) Per person EVSI (C) Total EVSI (D) =(A)*10 years*(C) ENBS (E) = (D) – (B) 1 Behavioural weight management 1 year 500 14 901 £1 543 500 £12.09 £1 801 614 £258 114 2 2000 14 901 £2 010 996 £15.49 £2 308 090 £297 094 3 2 years 500 14 901 £1 999 167 £17.06 £2 541 521 £542 354 4 2000 14 901 £2 568 667 £20.58 £3 067 305 £498 638 5 Specialist weight management 1 year 500 5 446 £1 543 500 £7.87 £428 359 -£1 115 141 6 2000 5 446 £2 010 996 £11.67 £635 660 -£1 375 336 7 2 years 500 5 446 £1 999 167 £12.60 £686 317 -£1 312 850 8 2000 5 446 £2 568 667 £16.12 £877 631 -£1 691 036 9 Diabetes prevention programme 1 year 500 53 000 £1 543 500 £22.17 £11 748 770 £10 205 270 10 2000 53 000 £2 010 996 £25.29 £13 405 200 £11 394 204 11 2 years 500 53 000 £1 999 167 £26.53 £14 058 948 £12 059 781 12 2000 53 000 £2 568 667 £29.39 £15 576 581 £13 007 914 13 Digital weight management 1 year 500 14 268 £1 543 500 £6.84 £975 931 -£567 569 14 2000 14 268 £2 010 996 £11.15 £1 590 882 -£420 114 15 2 years 500 14 268 £1 999 167 £11.36 £1 620 845 -£378 322 16 2000 14 268 £2 568 667 £14.16 £2 020 349 -£548 318 17 Diabetes Remission 1 year 500 1 000 £1 543 500 £11.89 £118 929 -£1 424 571 18 2000 1 000 £2 010 996 £16.29 £162 930 -£1 848 066 19 2 years 500 1 000 £1 999 167 £15.96 £159 648 -£1 839 519 20 2000 1 000 £2 568 667 £20.15 £201 509 -£2 367 158 21 All weight loss programme populations 1 year 500 86 728 £1 543 500 £12.29 £10 657 066 £9 113 566 22 2000 86 728 £2 010 996 £16.27 £14 110 069 £12 099 073 23 2 years 500 86 728 £1 999 167 £17.30 £15 006 737 £13 007 570 24 2000 86 728 £2 568 667 £20.47 £17 749 676 £15 181 009 The per patient Expected Value of Sample Information increases with larger sample size and duration of follow-up. The Expected Value of Sample Information is similar across RCT inclusion criteria. A trial in patients completing a diabetes prevention programme is estimated to be the most valuable per patient, whereas a trial in patients completing the digital weight loss programme is the least valued per patient. The per patient Expected Value of Sample Information across each of the proposed trial designs are reported in column C of Table 5. The population Expected Value of Sample Information varies across RCT inclusion criteria design. The all weight loss programme population has the largest population Expected Value of Sample Information due to the very large potential numbers of people completing weight loss services and eligible to receive the SWIM intervention following the trial. Whereas the populations with smaller expected eligible numbers, such as specialist weight management and digital weight management services report the lowest population Expected Value of Sample Information. The population Expected Value of Sample Information for all the proposed RCT designs are reported in column D of Table 5. The estimated RCT cost for each trial design is reported in column B of Table 5. Due to the large overheads in running a trial, increases in sample size and duration of follow-up result in modest increases in the estimate trial cost. After deducting the trial cost, the Expected Net Benefit of Sampling is reported in Column E of Table 5, and illustrated in Fig. 2 . All trials evaluating SWiM in the digital weight management population generated a negative Expected Net Benefit of Sampling, suggesting that investments in weight loss maintenance research in this population would not offer good value for money. The trial design generating the largest Expected Net Benefit of Sampling was the large trial with 2 years duration with an inclusion criteria recruiting participants from all weight loss services. This trial design is expected to generate approximately £15 million worth of benefits to the NHS. Discussion This analysis quantifies the current uncertainty in the cost-effectiveness of SWiM and demonstrates that investment in further research on intervention effectiveness offers good value for money. The analysis has found that SWiM could be cost-effective compared with usual care if implemented following five different weight management services. Despite low incremental cost-effectiveness ratios for SWiM, there is high uncertainty in these estimates in the absence of large scale RCT evidence. This uncertainty implies that there is a risk that usual care is a more efficient option. This risk can be reduced by collecting more evidence, and the trial design generating the largest EVSI would have a sample size of 2000, with two years of follow-up and inclusion criteria capturing all five weight management programmes. Although the analysis highlights the variation in cost-effectiveness of SWiM across patient populations, the trial design with the greatest ENBS was that which combined all five populations, because this increases the size of the future populations who may benefit from the intervention. A smaller trial of 500 participants in this population was also estimated to generate £9 million of benefits to the NHS, however the analysis also demonstrates that there is value in collecting effectiveness evidence in behavioural weight management and diabetes prevention populations individually. The size of the population estimated to benefit from the intervention in this analysis are challenging due to limited national monitoring, and are likely to be dynamic due to population changes and service funding. However, given the large difference between the estimated value of research and trial costs the trials would require approximately 14,000 patients per year to enrol with the service justify investment in further research. In the UK, demand for weight management is highly likely to exceed this estimate. This is the first study to use value of information methods to assess the value of research into weight maintenance interventions. Previously researchers have employed EVSI analyses to estimate sample size estimates for a proposed trial( 29 , 30 ). Comparisons of per person EVSI estimates are challenging due to differences in study setting, and methodological approaches. Nevertheless, a crude comparison with these studies suggests show that the maximum value £29 per patient for a SWiM trial design is similar to the maximum £27 and £24 per patient reported in these previous EVSI studies ( 29 , 30 ) and the duration of follow-up and inclusion criteria were also considered important trial design features. The value to society of the SWIM RCT is similar in magnitude to a previous EVSI analysis. The analysis assessed the value of cost data collection study, which was valued at £11 million ( 29 ). Despite recent research demonstrating and advocating the use of efficient methods for conducting EVSI analyses in recent years there are very few examples within the literature. A limitation of the analysis was that we did not elicit differences in weight for SWIM compared with usual care conditional on patient characteristics (e.g. baseline BMI, gender, socioeconomic status, comorbidities) during the expert elicitation exercise. The elicitation exercise required substantial engagement from experts, and was time consuming, particularly because the exercise adheres to guidelines ( 21 ). Our elicitation exercise was conducted within a 4-hour workshop, suggesting that each elicited value requires approximately 1-1.5 hours. Due to participant availability, we were only able to recruit 4 participants for this exercise. A larger and longer workshop would improve confidence in the distribution and incorporate factors impacting on the probability distribution estimate. It was necessary to use approximation methods to estimate the EVSI in this study. Until recently, EVSI calculations were extremely computationally expensive, because they required nested simulation methods. As such, it would not be possible to generate EVSI estimates for a complicated microsimulation model, as used here. The accuracy and efficiency of approximation methods have been tested and compared and found to generate accurate EVSI estimates ( 31 ). The conditions for this analysis, including the trial design and model simulation time, were consistent with the use of the regression-based method. However, it should be noted that in other studies the PSA samples used to test the approximation methods were larger than the 3000 samples generated for this study. Given the complexity of this model it was not feasible to generate a larger PSA for the EVSI analysis, and stability tests confirmed this was not necessary. The adoption of EVSI approximation techniques has substantially reduced the computational burden of value of information analyses. As such, we have been able to evaluate twenty-four trial designs within this study. Previous studies have highlighted the skills and experience required to implement EVSI analysis as barriers to implementing EVSI methods ( 23 ). Open access tools, such as SAVI used here, increase the opportunity for health economists to adopt these techniques. Therefore, we believe that more widespread use of EVSI is achievable in the future. Conclusions Value of Information analysis provides a formal framework to assess decision uncertainty prior to commissioning a trial to assess whether it offer good value for money given limited resources. In this example, we have demonstrated how practical decisions regarding the design of the trial can be informed by health economic modelling. Systematic use of these methods would enable efficient allocation of research funding to high value research ideas. Declarations Competing Interests This work was supported by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (RP-PG-0216-20010). PB, SR, SB, report no conflicts of interest. ALA is on the Scientific Advisory Board for WW and is Principal Investigator on two publicly funded investigator-led trials where the intervention is provided by WW. Author contributions PB was responsible for designing the analysis plan, elicitation study, model design and writing the report. KP was responsible for updating the model, conducting the analysis and writing the technical appendix. DP contributed to the analysis plan, model development, elicitation study and manuscript. KR contributed to the design of the elicitation study and manuscript. SB contributed to the analysis plan, development of the model and manuscript. CT contributed to the analysis plan, development of the model, and commenting on manuscript drafts. AA contributed to the development of the analysis plan and manuscript. SG contributed to the development of the analysis plan and manuscript. AB contributed to the development of the analysis plan, model development and manuscript. Acknowledgements We thank Brenda A Oulo and Catherine Gallagher for support in preparing documents for the expert elicitation. We thanks Dr Carly Hughes, Dr Michelle Harvie, Dr Helen Paretti and Dr Sarah Cotterill for participating in the expert elicitation. This work was supported by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (RP-PG-0216-20010). Data Availability Statement Most inputs to the model are from published sources and results from the expert elicitation are reported in the supplementary material. The anonymised Health Survey for England datasets can be accessed via the UK Data Service. Researchers interested in accessing the datasets will need to register with UK Data Service to access the data. References Ahern AL, Breeze P, Fusco F, Sharp SJ, Islam N, Wheeler GM, et al. Effectiveness and cost-effectiveness of referral to a commercial open group behavioural weight management programme in adults with overweight and obesity: 5-year follow-up of the WRAP randomised controlled trial. The Lancet Public Health. 2022;7(10):e866-e75. Tahrani AA, Morton J. Benefits of weight loss of 10% or more in patients with overweight or obesity: a review. Obesity. 2022;30(4):802–40. National Institute for Health and Care Excellence. Weight management: lifestyle services for overweight or obese adults. 2014 [Public Health Guideline PH53:[ Group LAR. Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. New England journal of medicine. 2013;369(2):145–54. Richards R, Jones RA, Whittle F, Hughes CA, Hill AJ, Lawlor ER, et al. Development of a web-based, guided self-help, acceptance and commitment therapy–based intervention for weight loss maintenance: evidence-, theory-, and person-based approach. JMIR Formative Research. 2022;6(1):e31801. Ahern AL, Richards R, Jones RA, Whittle F, Mueller J, Woolston J, et al. Acceptability and feasibility of an acceptance and commitment therapy-based guided self-help intervention for weight loss maintenance in adults who have previously completed a behavioural weight loss programme: the SWiM feasibility study protocol. BMJ open. 2022;12(4):e058103. Excellence NIfHaC. Developing NICE Guidelines: The Manual [Internet]. 2015. Claxton K, Posnett J. An economic approach to clinical trial design and research priority-setting. Health economics. 1996;5(6):513–24. Hayes AJ, Leal J, Gray AM, Holman RR, Clarke PM. UKPDS outcomes model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia. 2013;56(9):1925–33. Clarke PM, Gray AM, Briggs A, Farmer AJ, Fenn P, Stevens RJ, et al. A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68). Diabetologia. 2004;47(10):1747–59. Breeze P, Squires H, Chilcott J, Stride C, Diggle PJ, Brunner E, et al. A statistical model to describe longitudinal and correlated metabolic risk factors: the Whitehall II prospective study. J Public Health (Oxf). 2015. National Institute for Health and Care Excellence (NICE)NICE health technology evaluations: the manual. 2023 [updated 31st October 2023. Health Survey for England. NHS Digital2019. National Institute for Health and Care Excellence. Obesity: identification, assessment and management. 2014 [updated 26 July 2023. NHS Diabetes Prevention Programme (NHS DPP) [ NHS Path to Remission Programme [ The NHS Digital Weight Management Programme [ Lovelace R, Dumont M. Spatial microsimulation with R: Chapman and Hall/CRC; 2017. Mueller J, Jones RA, Richards R, Woolston J, Whittle F, Hill AJ, et al. Feasibility and acceptability of an acceptance-based guided self-help programme for weight loss maintenance. Appetite. 2023;189:106907. Holzhauer B, Hampson LV, Gosling JP, Bornkamp B, Kahn J, Lange MR, et al. Eliciting judgements about dependent quantities of interest: The SHeffield ELicitation Framework extension and copula methods illustrated using an asthma case study. Pharmaceutical Statistics. 2022;21(5):1005–21. Bojke L, Soares M, Claxton K, Colson A, Fox A, Jackson C, et al. Developing a reference protocol for structured expert elicitation in health-care decision-making: a mixed-methods study. Health Technology Assessment (Winchester, England). 2021;25(37):1. Ortner Hadžiabdić M, Mucalo I, Hrabač P, Matić T, Rahelić D, Božikov V. Factors predictive of drop-out and weight loss success in weight management of obese patients. Journal of human nutrition and dietetics. 2015;28:24–32. Kunst N, Wilson EC, Glynn D, Alarid-Escudero F, Baio G, Brennan A, et al. Computing the expected value of sample information efficiently: practical guidance and recommendations for four model-based methods. Value in Health. 2020;23(6):734–42. Strong M, Oakley JE, Brennan A, Breeze P. Estimating the expected value of sample information using the probabilistic sensitivity analysis sample: a fast, nonparametric regression-based method. Medical Decision Making. 2015;35(5):570–83. Strong M, Breeze P, Thomas C, Brennan A. Sheffield Accelerated Value of Information (SAVI). University of Sheffield Available at. 2014. Office for Health Improvement and Disparities (OHID) Adult tier 2 weight management services provisional data for April 2021 to December 2022 (experimental statistics). 2023. Jennings A HC, Kumaravel B, Bachmann MO, Steel N, Capehorn M, Cheema K. Evaluation of a multidisciplinary T ier 3 weight management service for adults with morbid obesity, or obesity and comorbidities, based in primary care.. Clinical obesity. 2014;4(5):254–66. Howarth E BP, Kontopantelis E, Soiland-Reyes C, Meacock R, Whittaker W, Cotterill S. ‘Going the distance’: an independent cohort study of engagement and dropout among the first 100 000 referrals into a large-scale diabetes prevention program.. BMJ Open Diabetes Research and Care 2020;8(2):e001835. Wilson EC, Wastlund D, Moraitis AA, Smith GC. Late pregnancy ultrasound to screen for and manage potential birth complications in nulliparous women: a cost-effectiveness and value of information analysis. Value in Health. 2021;24(4):513–21. Pham CT, Visvanathan R, Strong M, Wilson EC, Lange K, Dollard J, et al. Cost-Effectiveness and Value of Information Analysis of an Ambient Intelligent Geriatric Management (AmbIGeM) System Compared to Usual Care to Prevent Falls in Older People in Hospitals. Applied Health Economics and Health Policy. 2023;21(2):315–25. Heath A, Kunst N, Jackson C, Strong M, Alarid-Escudero F, Goldhaber-Fiebert JD, et al. Calculating the expected value of sample information in practice: considerations from 3 case studies. Medical Decision Making. 2020;40(3):314–26. Additional Declarations There is NO conflict of interest to disclose Supplementary Files FileS1V1.0SupplementaryMethodsSWiM.docx Cite Share Download PDF Status: Published Journal Publication published 22 May, 2025 Read the published version in International Journal of Obesity → Version 1 posted Editorial decision: revise 24 Oct, 2024 Review # 2 received at journal 23 Oct, 2024 Reviewer # 2 agreed at journal 18 Oct, 2024 Review # 1 received at journal 30 Aug, 2024 Reviewer # 1 agreed at journal 19 Aug, 2024 Reviewers invited by journal 14 Aug, 2024 Submission checks completed at journal 13 Aug, 2024 Editor assigned by journal 12 Aug, 2024 First submitted to journal 12 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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15:50:28\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4901753/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4901753/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1038/s41366-025-01804-7\",\"type\":\"published\",\"date\":\"2025-05-22T04:00:00+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":66426263,\"identity\":\"67f1a259-c9f3-4655-b605-bb0e986da454\",\"added_by\":\"auto\",\"created_at\":\"2024-10-11 17:26:05\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":22560,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePre-Trial estimation of uncertainty in treatment effect for difference in weight for SWiM versus Usual Care at 12 months and 24 months based on structured elicitation with 4 experts\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4901753/v1/c0dd46513bcd2f571de0159f.png\"},{\"id\":66426262,\"identity\":\"57ffcac1-adb3-44cd-8788-be2ca9d4fdd6\",\"added_by\":\"auto\",\"created_at\":\"2024-10-11 17:26:05\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":35491,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEstimated expected Net benefit of Sampling (ENBS) by trial design\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4901753/v1/da11bfc5ace30538d53ff6eb.png\"},{\"id\":83328818,\"identity\":\"70e72529-7599-456f-a22d-dcc7ab9b4203\",\"added_by\":\"auto\",\"created_at\":\"2025-05-23 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money, from a policy maker prospective, of 24 randomised controlled trial designs for an online weight maintenance guided self-help intervention: An expected value of sample information analysis.\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eBehavioural weight management programmes are a cost-effective weight loss intervention (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). Weight loss can lead to health improvements and has been shown to reduce the risk of diabetes, and improve the management of type 2 diabetes (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). As such, in the United Kingdom the National Institute for Health and Care Research (NICE) recommends multicomponent weight management services for people with overweight or obesity (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). The long-term health benefits of weight management are more likely to be sustained when weight loss is maintained (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). However, weight loss programmes often follow a pattern of substantial weight loss in the initial stages followed by some or total weight regain (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e). Publicly funded services specifically designed for weight maintenance are not available to support patients in the UK. Weight management could be improved if effective weight maintenance interventions were available to support adults to maintain their weight loss and the associated health benefits.\\u003c/p\\u003e \\u003cp\\u003eAcceptance-based behavioural interventions have superior long-term weight outcomes compared to standard behavioural programmes.(\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e) A rigorous process was taken to develop Supporting Weight Management (SWiM), a web-based, guided self-help intervention that uses Acceptance and Commitment Therapy (ACT).(\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e) SWiM was designed to be implemented at scale and therefore uses digital technology and non-specialist guides to reduce the resources needed to deliver an ACT-based intervention. A feasibility study evaluating the SWiM programme assessed the feasibility and effectiveness of the intervention.(\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e) However, the effectiveness and cost-effectiveness is uncertain, and a full scale randomised controlled trial (RCT) is needed to generate estimates of intervention effectiveness that can inform policy decisions to commission a new service.\\u003c/p\\u003e \\u003cp\\u003e Health economic models are commonly used to extrapolate short-term intervention effectiveness evidence into robust estimates of the lifetime costs, Quality Adjusted Life Years (QALYs) and incremental cost-effectiveness to inform national guidelines.(\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e) This method provides a formal process to evaluate whether a new intervention provides value for money compared with other policy options. This decision will always be made with uncertainty and the analysis should communicate the uncertainty to policymakers. Where decisions are highly uncertain there is a risk that the analysis will make an incorrect recommendation and it is advisable to collect more data prior to implementing the new intervention at scale. Health economic modelling can also be used to inform a decision to collect more data. Value of Information analysis allows new research proposals to be valued in terms of the anticipated reduction in uncertainty in cost-effectiveness estimates used for policy decision-making. Expected value of sample information (EVSI) is a method that quantifies the reduction in uncertainty of undertaking a specific research design(\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). Economists can then calculate the expected net benefit of sampling (ENBS), which is the difference between the expected value of the research and the expected research costs. Additionally alternative designs can be compared to identify designs that maximise the ENBS. Taken together these approaches offer research funders a framework to improve research funding allocation decisions for quantitative research informing economic evaluations and can guide research design.\\u003c/p\\u003e \\u003cp\\u003eIn this study we estimate the expected value of an RCT to provide evidence on the effectiveness of a weight maintenance intervention. We have identified three important design options for the RCT: sample size; duration of follow-up and inclusion criteria. This study aimed to evaluate the EVSI and ENBS of twenty-four RCT designs for SWIM.\\u003c/p\\u003e \"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003eOverview\\u003c/p\\u003e \\u003cp\\u003eHealth economic models use probabilistic sensitivity analysis to describe the uncertainty about whether a new intervention is cost-effective compared to a comparator. The analysis may indicate that the intervention is likely to be cost-effective, but the estimate is not certain. In PSA, model parameter inputs are represented as distributions around the point estimate to capture the uncertainty in the analysis. This is used to estimate the likelihood that an intervention meets a well-established willingness to pay threshold for a cost per quality adjusted life year (QALY) gained. New research will reduce the uncertainty in model parameters and reduce the risk of a sub-optimal decision; i.e. a decision to fund an intervention based on an expected cost-effectiveness estimate when the true cost-effectiveness is above the cost-per-QALY threshold. The strength and precision of evidence generated by a new RCT will depend on the trial design. In this value of information analysis, we simulated RCT data using prior expectations about the likely effectiveness of the intervention to generate a range of potential outcomes from each of 24 potential trial designs. Trial designs were varied by sample size, duration of follow-up and population inclusion criteria. Trial designs that collect more data will increase precision in their estimates and have greater value because they reduce more uncertainty. The analysis values the reduction in uncertainty provided by the simulated RCT data in terms of the increase in the expected health and economic benefit for a single patient. This is consistent with the output from health economic analyses, but this only describes the benefit at the patient level. The decision-maker should consider the overall benefit to society. The total value to society of the RCT is conditional on how many patients will receive the intervention and over what time horizon.\\u003c/p\\u003e \\u003cp\\u003eThe Health Economic model\\u003c/p\\u003e \\u003cp\\u003eThe School for Public Health Research (SPHR) Diabetes prevention model (version 5.2) is used here to assess the cost-effectiveness of SWiM versus standard care from an NHS and Personal Social Services perspective (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). The model is an individual patient level microsimulation based on the evolution of personalised trajectories for metabolic factors, including body mass index (BMI) and HbA1c. It uses existing statistical models from the UKPDS (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e), and Whitehall II (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e), and their links to major health outcomes. The model simulates the risk of type 2 diabetes, or diabetes related complications depending on the individual\\u0026rsquo;s characteristics and metabolic health status. Full details of the model and the sources of data for the uncertain parameters are provided in the supplementary appendix. The model can be used to assess the long-term cost-effectiveness of weight loss interventions in the UK by analysing the health impact of weight maintenance and HbA1c between an intervention and do-nothing option, and cost impact of the intervention. The lifetime discounted costs and QALYs are used to generate incremental cost-effectiveness ratios, and incremental Net Monetary Benefit assuming a willingness to pay threshold of \\u0026pound;20,000 (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). The SWIM intervention has been developed to support patients who have recently completed a weight management programme. The baseline population entering the model uses individual-level data from the adult Health Survey for England 2018 population (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e). For each RCT inclusion criteria option individuals sampled to be representative of the sociodemographic and medical history of participants completing weight management services currently recommended or commissioned in the UK. Five services that were identified to precede SWIM were behavioural weight management intervention (Tier 2 weight management (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e)), specialist weight management (Tier 3 weight management (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e)), Diabetes prevention programme (NHS Diabetes Prevention Programme(\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e)), Diabetes remission (NHS Path to remission (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e)), and Digital weight management (NHS Digital weight management (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e)). A sixth population described an \\u0026lsquo;all weight management\\u0026rsquo; population in which participants were referred from any of the five services to receive SWIM. Iterative proportional fitting methods were used to generate populations entering the model to align with different patient populations completing these weight management programmes that could be eligible for the new intervention (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). Details of the data and methods used to describe population characteristics are detailed in the supplementary appendix. In each analysis the population entering the model were assumed to have completed a weight loss programme. The effect\\u003c/p\\u003e \\u003cp\\u003eof the initial weight loss programme was simulated conditional on the simulated individual\\u0026rsquo;s characteristics, but not assumed to impact the effectiveness of SWIM. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e reports summary statistics for each eligible population evaluated in the model.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBaseline characteristics for the 50,000 simulated individuals \\u0026amp; estimated annual eligible population in England for each of 6 defined population trial inclusion criteria options\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c7\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003ePopulation inclusion criteria - People who recently completed\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBehavioural weight management\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSpecialist weight management\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDiabetes prevention programme\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eDigital weight management\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eDiabetes remission\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eAll weight loss programme populations\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNumber (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNumber (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNumber (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNumber (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eNumber (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNumber (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12 127 (24%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14 984 (30%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e22 464 (45%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e16 293 (33%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e23 419 (47%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e16 942 (33.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e37 873 (76%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e35 016 (70%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e27 536 (55%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e33 707 (67%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e26 581 (53%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e33 058 (66.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWhite\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e43 435\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e43 687 (87%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e44 413 (89%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e41 810 (84%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e40 054 (80%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e43 028 (86.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBlack African/Caribbean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1 988\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2 592 (5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 254 (3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2 591 (5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4 297 (9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2 431 (4.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAsian\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3 178\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2 880 (6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3 487 (7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4 775 (10%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4 701 (9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e34 77 (7.0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther ethnicity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1 399\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e841 (2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e846 (2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e824 (2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e948 (2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1 064 (2.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUnderweight/Healthy (\\u0026lt;\\u0026thinsp;25kg/m2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9 120 (18%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0 (0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e5 269 (11%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e27 43 (5.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOverweight (25\\u0026ndash;30)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7 721 (15%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e17 711 (35%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e697 (1.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e8 803 (18%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e8 321 (16.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eObesity class 1 and 2 (30\\u0026ndash;40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e30 200 (60%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16 281 (33%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e23 169 (46%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e33 420 (66.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e35 928 (72%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e25 554 (51.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eObesity class 3 (40+)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12 079 (24%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e33 719 (67%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e15 883 (31.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e13 382 (26.8%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCurrent smoker\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7 967 (16%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7 169 (14%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7 789 (16%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4 589 (9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e6 738 (13%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e7 575 (15.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eType 2 diabetes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3 307(7%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16 088 (32.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e19 089 (38.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e50 000 (100%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e14 541 (29.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNon-diabetic hyperglycaemia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5 236 (10.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 634 (11.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e50 000 (100%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e25 467 (10.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e13 209 (26.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNormoglycemia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e41 457 (82.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28 278 (56.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0 (0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5 444 (50.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0 (0%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e22 250 (44.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHypertension\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6 215 (12%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11 639 (23%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13 888 (28%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e36 308 (73%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e16 458 (33%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e11 108 (22.2%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStatins\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4 330 (9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9 157 (18%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14 730 (29%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e16 612 (33%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e18 114 (36%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e10 135 (20.3%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean (SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMean (SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMean (SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMean (SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMean (SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eMean (SD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e50.4 (15.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e53.4 (15.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e64.3 (12.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e59.5 (13.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e60.5 (12.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e55.7 (15.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI (kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e35.23 (6.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e41.47 (5.78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30.11 (5.87)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e37.43 (6.29)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e33.42 (6.68)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e35.15 (7.26)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHbA1c (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.62 (0.72)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.14 (1.18)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.12 (0.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.34 (1.24)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e7.46 (1.40)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e6.21 (1.16)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSBP (mmHg)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e126.36 (16.97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e129.98 (18.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e126.81 (16.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e131.65 (17.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e132.62(15.87)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e128.49 (16.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCHOL (mmol/l)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.19 (1.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.00 (1.03)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.14 (1.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.97 (1.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.62 (1.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e5.04 (1.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHDL (mmol/l)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.40 (0.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.31 (0.36)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.37 (0.39)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.38 (0.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.30 (0.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.36 (0.38)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHeight (m)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.65 (0.09)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.64 (0.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.65 (0.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.64 (0.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.66 (0.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.65 (0.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEstimated annual eligible population in England\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e14 901\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5 446\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e53 000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14 000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1 000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e86 728\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c7\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI Body Mass Index following weight management programme; HDL High-density lipoprotein; SBP systolic blood pressure; SD standard deviation\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePre-Trial Uncertainty in Health Economic outcomes based on 3000 probabilistic sensitivity analysis samples of the prior parameter values incorporating the structured elicitation from 4 experts on the uncertainty in effectiveness of SWiM intervention versus usual care\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrior Mean Incremental Costs\\u003c/p\\u003e \\u003cp\\u003e[95% credible interval]\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePrior Mean Incremental QALYS\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePrior Mean Incremental Cost per QALY (ICER)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNet Monetary Benefit (QALY valued at \\u0026pound;20,000)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eProbability cost-effective at \\u0026pound;20,000 per QALY threshold\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBehavioural weight management\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026pound;94.63\\u003c/p\\u003e \\u003cp\\u003e[\\u0026pound;-232.90, \\u0026pound;195.87]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.019\\u003c/p\\u003e \\u003cp\\u003e[0.000, 0.046]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026pound;4 940.31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026pound;287.37\\u003c/p\\u003e \\u003cp\\u003e[\\u0026pound;-188.02, \\u0026pound;1010.26]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.76\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSpecialist weight management\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e42.14\\u003c/p\\u003e \\u003cp\\u003e[\\u0026pound;-418.94, \\u0026pound;185.74]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.023\\u003c/p\\u003e \\u003cp\\u003e[0.001, 0.062]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026pound;1 852.32\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026pound;411.86\\u003c/p\\u003e \\u003cp\\u003e[\\u0026pound;-171.01, \\u0026pound;1603.16]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.78\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes Prevention Programme\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026pound;105.72\\u003c/p\\u003e \\u003cp\\u003e[\\u0026pound;-141.59, \\u0026pound;177.62]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.013\\u003c/p\\u003e \\u003cp\\u003e[0.000, 0.038]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026pound;7 834.89\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026pound;164.16\\u003c/p\\u003e \\u003cp\\u003e[\\u0026pound;-167.37, \\u0026pound;877.18]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.65\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDigital weight management\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026pound;28.76\\u003c/p\\u003e \\u003cp\\u003e[\\u0026pound;-447.58, \\u0026pound;181.10]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.024\\u003c/p\\u003e \\u003cp\\u003e[0.001, 0.080]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026pound;1 181.05\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026pound;459.23\\u003c/p\\u003e \\u003cp\\u003e[\\u0026pound;-167.99, \\u0026pound;2003.37]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.80\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDiabetes remission\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026pound;9.87\\u003c/p\\u003e \\u003cp\\u003e[\\u0026pound;-512.90, \\u0026pound;172.19]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.017\\u003c/p\\u003e \\u003cp\\u003e[0.000, 0.050]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026pound;579.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026pound;332.13\\u003c/p\\u003e \\u003cp\\u003e[\\u0026pound;-159.71, \\u0026pound;1487.70]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.74\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAll patients\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026pound;71.03\\u003c/p\\u003e \\u003cp\\u003e[\\u0026pound;-300.54, \\u0026pound;183.95]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.018\\u003c/p\\u003e \\u003cp\\u003e[0.001, 0.045]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026pound;3 885.30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026pound;294.97\\u003c/p\\u003e \\u003cp\\u003e[\\u0026pound;-173.07, \\u0026pound;1162.00]\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003ePrior parameter distributions\\u003c/p\\u003e \\u003cp\\u003eIn line with terminology used in Bayesian statistics, we refer to parameters that will be updated with trial data as prior parameter distributions. The prior parameter distributions were specified in line with current beliefs on the effectiveness of the intervention.\\u003c/p\\u003e \\u003cp\\u003eThe SWIM feasibility study provided an estimate of the mean difference in weight between the SWiM intervention and usual care after 6 months (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e). This data is from a small feasibility study and the effectiveness beyond 6 months is unknown so we consulted with stakeholders with expertise in weight management to characterise prior parameters for the weight difference over time. We employed the Sheffield ELicitation Framework (SHELF) methodology to elicit expert opinions on the distribution of uncertainty in intervention effects on weight(\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e), and followed best practice guidance(\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e). We circulated an evidence dossier to workshop participants based on the SWIM feasibility study (6 month weight outcome) with evidence synthesis of similar interventions (ACT and behavioural weight maintenance interventions) to prior to the workshop. The evidence dossier identified that behavioural weight maintenance interventions reduce weight regain, and there is some evidence that ACT-based interventions are more effective than standard behavioural approaches. The workshop aimed to estimate probability distributions for the difference in weight at 12 and 24 months for SWiM compared with usual care (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e). The difference in weight at 24 months was elicited as conditional on weight difference at 12 months, such that larger treatment effects at 12 months are associated with larger treatment effects at 24 months. The outcomes of the expert elicitation are reported in Section 11 of the supplementary appendix. The final prior parameter distributions for weight are illustrated in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eWe assumed that the effects of SWiM on change in HbA1c would be conditional on weight loss. We used associations between these outcomes derived from statistical analyses of two weight management randomised controlled trials. The association was conditional on diabetes status and alternative equations were used for individuals with non-hyperglycaemic, non-diabetic hyperglycaemia and type 2 diabetes (Supplementary Information: Section 11). The intervention cost for SWIM was estimated to be \\u0026pound;298 and no cost was assigned to usual care. The breakdown of costs associated with the intervention are reported in Section 12 of the supplementary appendix.\\u003c/p\\u003e \\u003cp\\u003eSimulating the proposed RCT designs\\u003c/p\\u003e \\u003cp\\u003eThe analysis considered twenty-four trial design options combining two sample size options (500 participants, 2000 participants), two duration of follow-up options (12 months, 24 months) and six inclusion criteria options (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). For the inclusion criteria we estimated the EVSI for populations completing five behavioural and weight management programmes representative of the broad range of behavioural programmes available and illustrated by those currently funded in the UK. We also evaluated the EVSI for a trial combining a population from all five programmes (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eResults from simulation of 3000 possible future trial outcomes: Simulated mean difference for SWiM versus Usual Care in Weight and HbA1c across 24 proposed trial designs\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003ePopulation inclusion criteria\\u003c/p\\u003e \\u003cp\\u003ePeople who recently completed \\u0026hellip;\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eDuration of follow-up\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eSample size\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e12 Months\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e24 Months\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMean Change in Weight (kg)\\u003c/p\\u003e \\u003cp\\u003e(standard error)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMean Change in Hba1c (%)\\u003c/p\\u003e \\u003cp\\u003e(standard error\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMean Change in Weight (kg)\\u003c/p\\u003e \\u003cp\\u003e(standard error)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eMean Change in Hba1c (units)\\u003c/p\\u003e \\u003cp\\u003e(standard error\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eBehavioural weight management\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e1 year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.953 (0.034)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.079 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.957 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.088 (0.003)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e2 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.953 (0.034)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.079 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.619 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.069 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.957 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.088 (0.003)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.606 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.065 (0.003)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eSpecialist weight management\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e1 year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.952 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.132 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.953 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.127 (0.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e2 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.952 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.132 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.606 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.109 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.953 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.127 (0.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.604 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.105 (0.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eDiabetes prevention programme\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e1 year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.953 (0.034)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.102 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.952 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.106 (0.003)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e2 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.953 (0.034)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.102 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.610 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.095 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.952 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.106 (0.003)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.607 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.083 (0.003)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eDigital weight management\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e1 year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.953 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.147 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.950 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.143 (0.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e2 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.953 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.147 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.603 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.117 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.950 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.143 (0.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.605 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.117 (0.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eDiabetes remission\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e1 year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.954 (0.034)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.277 (0.008)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.947 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.272 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e2 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.954 (0.034)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.277 (0.008)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.604 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.217 (0.007)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.947 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.272 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.606 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.217 (0.005)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eAll weight loss programme populations\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e1 year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.960 (0.034)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.131 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.950 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.135 (0.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e2 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.960 (0.034)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.131 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.612 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.104 (0.006)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.950 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.135 (0.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-1.604 (0.033)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.108 (0.004)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c7\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eStandard deviations used in sampling are calculated based on the sample size, with an assumed total participant loss to follow-up of 30% per trial independent of trial design.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe difference in weight and HbA1c over time for SWIM compared with usual care were assumed to be observable in an RCT to provide data on up to 4 quantities (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) difference in weight at 12 months, (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) difference in HbA1c at 12 months, (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e) difference in weight at 24 months, and (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e) difference in HbA1c at 24 months. We simulated RCT data to predict the four observable quantities from an RCT using Microsoft Excel (Version 3202). The mean change in weight at 12 months and 24 months were sampled from the prior parameter distribution. Variability in the weight was assumed with a standard deviation of 6.4 to be consistent with data from a previous weight management trial (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). The sampled prior information for the relationships between weight and HbA1c by diabetes status was used to estimate the mean change in HbA1c for the population. The standard deviation for HbA1c was 5.95 based on a previous weight loss trial (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). For all study designs we assume that 30% of participants would be lost to follow-up (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eThe EVSI and ENBS analysis\\u003c/p\\u003e \\u003cp\\u003eWe used an efficient regression-based approximation method, that has been previously tested and evaluated, to generate EVSI estimates (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e). Approximation methods are less computationally demanding processes that are compatible with more complex simulation approaches (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e). The method required the following steps:\\u003c/p\\u003e \\u003cp\\u003e \\u003col\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eWe performed a Probabilistic Analysis of the SPHR diabetes prevention model simulation to obtain a sample of 3000 uncertain parameter inputs and corresponding discounted costs, QALYs. The prior incremental Net Monetary Benefit for each programme population based on the prior parameter distributions, was calculated from the simulated costs and QALYs for each PSA parameter input sample.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eWe simulated summary trial outcomes for weight and HbA1c differences for all the proposed trials. For each trial design 3000 trial outcomes were sampled from a normal distribution in which each the mean was equal to prior parameter input sample and estimated standard error based on the trial sample size and standard deviation.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eWe used the Sheffield Accelerated Value of Information tool (SAVI) (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e) to apply the efficient regression based approach to calculate per person EVSI. This describes the extent to which the additional information from the study reduces the probability, and expected losses, of an inefficient decision. We extracted the values from SAVI to estimate the per-patient EVSI for the SWiM intervention.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eThe population Expected Value of Sample Information was calculated for the potential eligible population over the time horizon the intervention will be implemented. We estimate the potential eligible population as the estimated annual size of the population completing the weight management service. The per person ESVI was multiplied by the total population for a time horizon of 10 years.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003e5. The Expected Net Benefit of Sampling is calculated by subtracting the estimated cost of the RCT away from the population Expected Value of Sample Information.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e \\u003c/p\\u003e \\u003cp\\u003eFor each population sample we have run a PSA sample with 3000 sampled inputs for a population of 50,000 individuals to generate 3000 Incremental Net Monetary Benefit estimates based on prior parameter distributions with confidence intervals. The stability of the model across individuals and PSA runs are reported in Section 13 of the supplementary appendix..\\u003c/p\\u003e \\u003cp\\u003eThe EVSI calculation generates a value of information per person who will be affected by the decision. To make this useful for publicly funded research funders in the UK, we multiply the per person EVSIs with the potential eligible population. This estimates the total value of research from an NHS and personal social services perspective. We assume that the decision could benefit patients for 10 years to be consistent with other EVSI studies, but also to acknowledge observations that the intervention will be superseded by other policies in the long term. The number of people who benefit in each of the five programme populations listed above have been estimated (\\u003cspan additionalcitationids=\\\"CR27\\\" citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e), with full details provided in the supplementary appendix. The size of the sixth all weight management population is the sum of the five individual services, adjusting for overlapping populations (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). We estimate the per-person EVSI for a total of 24 trial designs, combining alternative combinations of sample size, duration of follow-up and inclusion criteria. For each trial design we also report the total EVSI over the eligible population and 10 year time horizon. We approximate the cost of an RCT including fixed overhead costs for research consumables, staff costs were conditional on the duration of follow-up, and per participant costs conditional on the number of participants recruited. The final costs estimate for the research designs are reported in Table\\u0026nbsp;5, with full details provided in Section 15 of the supplementary appendix.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eThe SWiM intervention is most likely to be cost-effective compared with usual care across all the weight management populations, however there is reasonable uncertainty given the effectiveness of the intervention has not been established in a full-scale RCT. The probability that the intervention is cost-effective at the \\u0026pound;20,000 willingness to pay threshold is between 65\\u0026ndash;80%. The results suggest that the expected Net Monetary Benefit of the intervention varies when offered to alternative weight management populations. SWIM following a digital weight management programme offers the highest expected Net Monetary Benefit and Diabetes Prevention Programme the lowest, due to variation in average BMI and prevalence of comorbidities. The diabetes remission population estimates greater cost-savings than other populations due to the additional cost saving from averting diabetes medication costs and delayed treatment escalation. The prior incremental expected net monetary benefit of the SWIM intervention across all five populations completing alternative weight management programmes and a combined population is reported in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003eThe simulated difference in weight and HbA1c are reported in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e for each RCT design. Increasing the sample size reduces the uncertainty in measure of weight and HbA1c, as illustrated in the standard errors reported in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. Increasing the duration of follow-up to 24 months provides in additional evidence to reduce uncertainty in the maintenance of weight and HbA1c changes.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eResults Comparing Expected Trial Cost with Expected Value of Sample Information for 24 possible RCT designs in 6 population subgroup inclusion criteria over a 10 year time horizon in England\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"9\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePopulation subgroup inclusion criteria \\u0026ndash; People who have recently completed \\u0026hellip;\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDuration of follow-up\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSample size\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eAnnual eligible population (A)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eEstimated RCT cost (B)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003ePer person EVSI (C)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eTotal EVSI (D) =(A)*10 years*(C)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eENBS (E) =\\u003c/p\\u003e \\u003cp\\u003e(D) 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colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026pound;498 638\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eSpecialist weight management\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e1 year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5 446\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026pound;1 543 500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e 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colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026pound;686 317\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-\\u0026pound;1 312 850\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5 446\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026pound;2 568 667\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026pound;16.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026pound;877 631\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-\\u0026pound;1 691 036\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eDiabetes prevention programme\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e1 year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e53 000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026pound;1 543 500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e 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colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026pound;14 058 948\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026pound;12 059 781\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e53 000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026pound;2 568 667\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026pound;29.39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026pound;15 576 581\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026pound;13 007 914\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eDigital weight management\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e1 year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14 268\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026pound;1 543 500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e 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colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026pound;1 620 845\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-\\u0026pound;378 322\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14 268\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026pound;2 568 667\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026pound;14.16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026pound;2 020 349\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-\\u0026pound;548 318\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eDiabetes Remission\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e1 year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1 000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026pound;1 543 500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026pound;11.89\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026pound;118 929\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-\\u0026pound;1 424 571\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1 000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026pound;2 010 996\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026pound;16.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026pound;162 930\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-\\u0026pound;1 848 066\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e2 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1 000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026pound;1 999 167\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026pound;15.96\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026pound;159 648\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-\\u0026pound;1 839 519\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1 000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026pound;2 568 667\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026pound;20.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026pound;201 509\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-\\u0026pound;2 367 158\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eAll weight loss programme populations\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e1 year\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e86 728\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026pound;1 543 500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026pound;12.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026pound;10 657 066\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026pound;9 113 566\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e86 728\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026pound;2 010 996\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026pound;16.27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026pound;14 110 069\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026pound;12 099 073\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e2 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e86 728\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026pound;1 999 167\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026pound;17.30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026pound;15 006 737\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026pound;13 007 570\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e86 728\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026pound;2 568 667\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026pound;20.47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026pound;17 749 676\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026pound;15 181 009\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe per patient Expected Value of Sample Information increases with larger sample size and duration of follow-up. The Expected Value of Sample Information is similar across RCT inclusion criteria. A trial in patients completing a diabetes prevention programme is estimated to be the most valuable per patient, whereas a trial in patients completing the digital weight loss programme is the least valued per patient. The per patient Expected Value of Sample Information across each of the proposed trial designs are reported in column C of Table\\u0026nbsp;5.\\u003c/p\\u003e \\u003cp\\u003eThe population Expected Value of Sample Information varies across RCT inclusion criteria design. The all weight loss programme population has the largest population Expected Value of Sample Information due to the very large potential numbers of people completing weight loss services and eligible to receive the SWIM intervention following the trial. Whereas the populations with smaller expected eligible numbers, such as specialist weight management and digital weight management services report the lowest population Expected Value of Sample Information. The population Expected Value of Sample Information for all the proposed RCT designs are reported in column D of Table\\u0026nbsp;5.\\u003c/p\\u003e \\u003cp\\u003eThe estimated RCT cost for each trial design is reported in column B of Table\\u0026nbsp;5. Due to the large overheads in running a trial, increases in sample size and duration of follow-up result in modest increases in the estimate trial cost. After deducting the trial cost, the Expected Net Benefit of Sampling is reported in Column E of Table\\u0026nbsp;5, and illustrated in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. All trials evaluating SWiM in the digital weight management population generated a negative Expected Net Benefit of Sampling, suggesting that investments in weight loss maintenance research in this population would not offer good value for money. The trial design generating the largest Expected Net Benefit of Sampling was the large trial with 2 years duration with an inclusion criteria recruiting participants from all weight loss services. This trial design is expected to generate approximately \\u0026pound;15\\u0026nbsp;million worth of benefits to the NHS.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis analysis quantifies the current uncertainty in the cost-effectiveness of SWiM and demonstrates that investment in further research on intervention effectiveness offers good value for money. The analysis has found that SWiM could be cost-effective compared with usual care if implemented following five different weight management services. Despite low incremental cost-effectiveness ratios for SWiM, there is high uncertainty in these estimates in the absence of large scale RCT evidence. This uncertainty implies that there is a risk that usual care is a more efficient option. This risk can be reduced by collecting more evidence, and the trial design generating the largest EVSI would have a sample size of 2000, with two years of follow-up and inclusion criteria capturing all five weight management programmes. Although the analysis highlights the variation in cost-effectiveness of SWiM across patient populations, the trial design with the greatest ENBS was that which combined all five populations, because this increases the size of the future populations who may benefit from the intervention. A smaller trial of 500 participants in this population was also estimated to generate \\u0026pound;9\\u0026nbsp;million of benefits to the NHS, however the analysis also demonstrates that there is value in collecting effectiveness evidence in behavioural weight management and diabetes prevention populations individually.\\u003c/p\\u003e \\u003cp\\u003eThe size of the population estimated to benefit from the intervention in this analysis are challenging due to limited national monitoring, and are likely to be dynamic due to population changes and service funding. However, given the large difference between the estimated value of research and trial costs the trials would require approximately 14,000 patients per year to enrol with the service justify investment in further research. In the UK, demand for weight management is highly likely to exceed this estimate.\\u003c/p\\u003e \\u003cp\\u003eThis is the first study to use value of information methods to assess the value of research into weight maintenance interventions. Previously researchers have employed EVSI analyses to estimate sample size estimates for a proposed trial(\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e). Comparisons of per person EVSI estimates are challenging due to differences in study setting, and methodological approaches. Nevertheless, a crude comparison with these studies suggests show that the maximum value \\u0026pound;29 per patient for a SWiM trial design is similar to the maximum \\u0026pound;27 and \\u0026pound;24 per patient reported in these previous EVSI studies (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e) and the duration of follow-up and inclusion criteria were also considered important trial design features. The value to society of the SWIM RCT is similar in magnitude to a previous EVSI analysis. The analysis assessed the value of cost data collection study, which was valued at \\u0026pound;11\\u0026nbsp;million (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e). Despite recent research demonstrating and advocating the use of efficient methods for conducting EVSI analyses in recent years there are very few examples within the literature.\\u003c/p\\u003e \\u003cp\\u003eA limitation of the analysis was that we did not elicit differences in weight for SWIM compared with usual care conditional on patient characteristics (e.g. baseline BMI, gender, socioeconomic status, comorbidities) during the expert elicitation exercise. The elicitation exercise required substantial engagement from experts, and was time consuming, particularly because the exercise adheres to guidelines (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e). Our elicitation exercise was conducted within a 4-hour workshop, suggesting that each elicited value requires approximately 1-1.5 hours. Due to participant availability, we were only able to recruit 4 participants for this exercise. A larger and longer workshop would improve confidence in the distribution and incorporate factors impacting on the probability distribution estimate.\\u003c/p\\u003e \\u003cp\\u003eIt was necessary to use approximation methods to estimate the EVSI in this study. Until recently, EVSI calculations were extremely computationally expensive, because they required nested simulation methods. As such, it would not be possible to generate EVSI estimates for a complicated microsimulation model, as used here. The accuracy and efficiency of approximation methods have been tested and compared and found to generate accurate EVSI estimates (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e). The conditions for this analysis, including the trial design and model simulation time, were consistent with the use of the regression-based method. However, it should be noted that in other studies the PSA samples used to test the approximation methods were larger than the 3000 samples generated for this study. Given the complexity of this model it was not feasible to generate a larger PSA for the EVSI analysis, and stability tests confirmed this was not necessary. The adoption of EVSI approximation techniques has substantially reduced the computational burden of value of information analyses. As such, we have been able to evaluate twenty-four trial designs within this study. Previous studies have highlighted the skills and experience required to implement EVSI analysis as barriers to implementing EVSI methods (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e). Open access tools, such as SAVI used here, increase the opportunity for health economists to adopt these techniques. Therefore, we believe that more widespread use of EVSI is achievable in the future.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eValue of Information analysis provides a formal framework to assess decision uncertainty prior to commissioning a trial to assess whether it offer good value for money given limited resources. In this example, we have demonstrated how practical decisions regarding the design of the trial can be informed by health economic modelling. Systematic use of these methods would enable efficient allocation of research funding to high value research ideas.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eCompeting Interests\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (RP-PG-0216-20010). PB, SR, SB, report no conflicts of interest. ALA is on the Scientific Advisory Board for WW and is Principal Investigator on two publicly funded investigator-led trials where the intervention is provided by WW.\\u003c/p\\u003e\\u003ch2\\u003eAuthor contributions\\u003c/h2\\u003e \\u003cp\\u003ePB was responsible for designing the analysis plan, elicitation study, model design and writing the report. KP was responsible for updating the model, conducting the analysis and writing the technical appendix. DP contributed to the analysis plan, model development, elicitation study and manuscript. KR contributed to the design of the elicitation study and manuscript. SB contributed to the analysis plan, development of the model and manuscript. CT contributed to the analysis plan, development of the model, and commenting on manuscript drafts. AA contributed to the development of the analysis plan and manuscript. SG contributed to the development of the analysis plan and manuscript. AB contributed to the development of the analysis plan, model development and manuscript.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgements\\u003c/h2\\u003e \\u003cp\\u003eWe thank Brenda A Oulo and Catherine Gallagher for support in preparing documents for the expert elicitation. We thanks Dr Carly Hughes, Dr Michelle Harvie, Dr Helen Paretti and Dr Sarah Cotterill for participating in the expert elicitation. This work was supported by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (RP-PG-0216-20010).\\u003c/p\\u003e\\u003ch2\\u003eData Availability Statement\\u003c/h2\\u003e \\u003cp\\u003eMost inputs to the model are from published sources and results from the expert elicitation are reported in the supplementary material. The anonymised Health Survey for England datasets can be accessed via the UK Data Service. Researchers interested in accessing the datasets will need to register with UK Data Service to access the data.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAhern AL, Breeze P, Fusco F, Sharp SJ, Islam N, Wheeler GM, et al. Effectiveness and cost-effectiveness of referral to a commercial open group behavioural weight management programme in adults with overweight and obesity: 5-year follow-up of the WRAP randomised controlled trial. The Lancet Public Health. 2022;7(10):e866-e75.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTahrani AA, Morton J. Benefits of weight loss of 10% or more in patients with overweight or obesity: a review. Obesity. 2022;30(4):802\\u0026ndash;40.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNational Institute for Health and Care Excellence. Weight management: lifestyle services for overweight or obese adults. 2014 [Public Health Guideline PH53:[\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGroup LAR. Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. New England journal of medicine. 2013;369(2):145\\u0026ndash;54.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRichards R, Jones RA, Whittle F, Hughes CA, Hill AJ, Lawlor ER, et al. Development of a web-based, guided self-help, acceptance and commitment therapy\\u0026ndash;based intervention for weight loss maintenance: evidence-, theory-, and person-based approach. JMIR Formative Research. 2022;6(1):e31801.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAhern AL, Richards R, Jones RA, Whittle F, Mueller J, Woolston J, et al. Acceptability and feasibility of an acceptance and commitment therapy-based guided self-help intervention for weight loss maintenance in adults who have previously completed a behavioural weight loss programme: the SWiM feasibility study protocol. BMJ open. 2022;12(4):e058103.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eExcellence NIfHaC. Developing NICE Guidelines: The Manual [Internet]. 2015.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eClaxton K, Posnett J. An economic approach to clinical trial design and research priority-setting. Health economics. 1996;5(6):513\\u0026ndash;24.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHayes AJ, Leal J, Gray AM, Holman RR, Clarke PM. UKPDS outcomes model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia. 2013;56(9):1925\\u0026ndash;33.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eClarke PM, Gray AM, Briggs A, Farmer AJ, Fenn P, Stevens RJ, et al. A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model (UKPDS no. 68). Diabetologia. 2004;47(10):1747\\u0026ndash;59.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBreeze P, Squires H, Chilcott J, Stride C, Diggle PJ, Brunner E, et al. A statistical model to describe longitudinal and correlated metabolic risk factors: the Whitehall II prospective study. J Public Health (Oxf). 2015.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNational Institute for Health and Care Excellence (NICE)NICE health technology evaluations: the manual. 2023 [updated 31st October 2023.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHealth Survey for England. NHS Digital2019.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNational Institute for Health and Care Excellence. Obesity: identification, assessment and management. 2014 [updated 26 July 2023.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNHS Diabetes Prevention Programme (NHS DPP) [\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNHS Path to Remission Programme [\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eThe NHS Digital Weight Management Programme [\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLovelace R, Dumont M. Spatial microsimulation with R: Chapman and Hall/CRC; 2017.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMueller J, Jones RA, Richards R, Woolston J, Whittle F, Hill AJ, et al. Feasibility and acceptability of an acceptance-based guided self-help programme for weight loss maintenance. Appetite. 2023;189:106907.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHolzhauer B, Hampson LV, Gosling JP, Bornkamp B, Kahn J, Lange MR, et al. Eliciting judgements about dependent quantities of interest: The SHeffield ELicitation Framework extension and copula methods illustrated using an asthma case study. Pharmaceutical Statistics. 2022;21(5):1005\\u0026ndash;21.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBojke L, Soares M, Claxton K, Colson A, Fox A, Jackson C, et al. Developing a reference protocol for structured expert elicitation in health-care decision-making: a mixed-methods study. Health Technology Assessment (Winchester, England). 2021;25(37):1.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOrtner Hadžiabdić M, Mucalo I, Hrabač P, Matić T, Rahelić D, Božikov V. Factors predictive of drop-out and weight loss success in weight management of obese patients. Journal of human nutrition and dietetics. 2015;28:24\\u0026ndash;32.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKunst N, Wilson EC, Glynn D, Alarid-Escudero F, Baio G, Brennan A, et al. Computing the expected value of sample information efficiently: practical guidance and recommendations for four model-based methods. Value in Health. 2020;23(6):734\\u0026ndash;42.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eStrong M, Oakley JE, Brennan A, Breeze P. Estimating the expected value of sample information using the probabilistic sensitivity analysis sample: a fast, nonparametric regression-based method. Medical Decision Making. 2015;35(5):570\\u0026ndash;83.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eStrong M, Breeze P, Thomas C, Brennan A. Sheffield Accelerated Value of Information (SAVI). University of Sheffield Available at. 2014.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOffice for Health Improvement and Disparities (OHID) Adult tier 2 weight management services provisional data for April 2021 to December 2022 (experimental statistics). 2023.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJennings A HC, Kumaravel B, Bachmann MO, Steel N, Capehorn M, Cheema K. Evaluation of a multidisciplinary T ier 3 weight management service for adults with morbid obesity, or obesity and comorbidities, based in primary care.. Clinical obesity. 2014;4(5):254\\u0026ndash;66.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHowarth E BP, Kontopantelis E, Soiland-Reyes C, Meacock R, Whittaker W, Cotterill S. \\u0026lsquo;Going the distance\\u0026rsquo;: an independent cohort study of engagement and dropout among the first 100 000 referrals into a large-scale diabetes prevention program.. BMJ Open Diabetes Research and Care 2020;8(2):e001835.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWilson EC, Wastlund D, Moraitis AA, Smith GC. Late pregnancy ultrasound to screen for and manage potential birth complications in nulliparous women: a cost-effectiveness and value of information analysis. Value in Health. 2021;24(4):513\\u0026ndash;21.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePham CT, Visvanathan R, Strong M, Wilson EC, Lange K, Dollard J, et al. Cost-Effectiveness and Value of Information Analysis of an Ambient Intelligent Geriatric Management (AmbIGeM) System Compared to Usual Care to Prevent Falls in Older People in Hospitals. Applied Health Economics and Health Policy. 2023;21(2):315\\u0026ndash;25.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHeath A, Kunst N, Jackson C, Strong M, Alarid-Escudero F, Goldhaber-Fiebert JD, et al. Calculating the expected value of sample information in practice: considerations from 3 case studies. Medical Decision Making. 2020;40(3):314\\u0026ndash;26.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"international-journal-of-obesity\",\"isNatureJournal\":false,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"ijo\",\"sideBox\":\"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)\",\"snPcode\":\"41366\",\"submissionUrl\":\"https://mts-ijo.nature.com/cgi-bin/main.plex\",\"title\":\"International Journal of Obesity\",\"twitterHandle\":\"@intjobesity\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Nature AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4901753/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4901753/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"Objective:\\r\\nTo analyse whether conducting a randomised controlled trial (RCT) to evaluate an online weight maintenance guided self-help intervention (the SWiM intervention) would offer good value for money in the United Kingdom. \\r\\nMethod\\r\\nWe examined 24 RCT designs by varying inclusion criteria (participants completing behavioural weight management, specialist-led weight management, diabetes prevention programme, type 2 diabetes remission, digital weight management, all weight management services), trial duration (1-2 years), and sample size (n=500 or 2000). Trial benefits were estimated by the method of expected value of sample information analysis using a health economic model. The model examines how the proposed intervention affects weight maintenance over time (with uncertainty), and generates estimated lifetime Quality Adjusted Life Years (QALYs) and National Health Service (NHS) costs. Structured expert elicitation with 4 experts was undertaken to quantify pre-trial uncertainty in the effectiveness of SWiM compared with usual care. All trial designs were simulated to estimate trial benefits: the reduction in the costs of an inefficient decision for future populations over 10 years. Trial designs offer value for money if trial benefits exceed trial costs. \\r\\nResults:\\r\\nFor three inclusion criteria options (groups recently completing ‘diabetes remission’, ‘digital weight management’ or ‘specialist weight management’), the cost of the proposed trials was estimated to exceed the estimated trial benefit (value of the reduction in decision uncertainty) over 10 years. For the other three inclusion criteria options (groups recently completed ‘behavioural weight management’, ‘diabetes prevention programme’, or ‘all weight loss programmes’), 12 trial designs produced greater benefits than costs. The optimal trial design option would include ‘all weight loss programmes’, with 2 years follow-up and sample size n=2000. \\r\\nConclusion:\\r\\nInvestment in an RCT to evaluate the SWiM intervention with two years of follow-up patients completing a range of weight loss interventions offers the greatest value to the NHS.\",\"manuscriptTitle\":\"Assessing the value for money, from a policy maker prospective, of 24 randomised controlled trial designs for an online weight maintenance guided self-help intervention: An expected value of sample information analysis.\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-10-11 17:26:00\",\"doi\":\"10.21203/rs.3.rs-4901753/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"revise\",\"date\":\"2024-10-24T15:34:08+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"This content is not available.\",\"date\":\"2024-10-23T14:26:27+00:00\",\"index\":2,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2024-10-18T09:06:12+00:00\",\"index\":2,\"fulltext\":\"This content is not available.\"},{\"type\":\"editorInvitedReview\",\"content\":\"This content is not available.\",\"date\":\"2024-08-30T13:16:33+00:00\",\"index\":1,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewerAgreed\",\"content\":\"This content is not available.\",\"date\":\"2024-08-19T10:09:20+00:00\",\"index\":1,\"fulltext\":\"This content is not available.\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-08-14T07:46:22+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-08-13T11:10:18+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-08-12T15:45:53+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"International Journal of Obesity\",\"date\":\"2024-08-12T15:45:53+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"international-journal-of-obesity\",\"isNatureJournal\":false,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"ijo\",\"sideBox\":\"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)\",\"snPcode\":\"41366\",\"submissionUrl\":\"https://mts-ijo.nature.com/cgi-bin/main.plex\",\"title\":\"International Journal of Obesity\",\"twitterHandle\":\"@intjobesity\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Nature AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"0cf8854c-0359-49e2-b8f1-e2029f49e187\",\"owner\":[],\"postedDate\":\"October 11th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":36027681,\"name\":\"Health sciences/Health care/Weight management\"},{\"id\":36027682,\"name\":\"Health sciences/Health care/Health policy\"},{\"id\":36027683,\"name\":\"Health sciences/Health care/Disease prevention\"}],\"tags\":[],\"updatedAt\":\"2025-05-23T07:09:25+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-4901753\",\"link\":\"https://doi.org/10.1038/s41366-025-01804-7\",\"journal\":{\"identity\":\"international-journal-of-obesity\",\"isVorOnly\":false,\"title\":\"International Journal of Obesity\"},\"publishedOn\":\"2025-05-22 04:00:00\",\"publishedOnDateReadable\":\"May 22nd, 2025\"},\"versionCreatedAt\":\"2024-10-11 17:26:00\",\"video\":\"\",\"vorDoi\":\"10.1038/s41366-025-01804-7\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41366-025-01804-7\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4901753\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4901753\",\"identity\":\"rs-4901753\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}