A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey

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Understanding community preferences for health screening services is vital for enhancing service delivery. We conducted a study to determine community preferences for health screening services for chronic diseases in Australia using a discrete choice experiment (DCE). This paper aims to present the development of the final DCE design using priors estimated from a survey. Methods A DCE was conducted in Australia. An online survey was administered to a general Australian population over 18. The final attribute list of five attributes with three levels each was designed. A D-efficient design with 30 pair-wise choice tasks was developed using a fractional factorial design. A pre-test was conducted to assess comprehension and understanding of the online DCE survey. The pilot survey aimed to compute priors (i.e. coefficients) associated with attributes. A multinomial logit model was used to analyse the pilot DCE data. Results The survey included 30 choice tasks in three blocks, with 119 participants responding. The best DCE design was selected based on D-error, with a lower D-error indicating the most efficient design. The pilot survey results indicated a strong preference for highly accurate screening tests, with coefficients for 85% and 95% accuracy being positive. Coefficients estimated from the pilot survey were used as priors to design the DCE choice tasks for the main survey. The final DCE design showed a notable improvement in the attribute level overlap compared to the design used for the pilot survey. Conclusions A rigorous approach was taken to develop a DCE survey that could effectively determine the preferences of the community for health screening services. The resulting DCE design consisted of 30 choice tasks presented in pairs and was deemed efficient enough to gather comprehensive information in the final survey. 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F1000Research 2025, 14 :96 ( https://doi.org/10.12688/f1000research.157017.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey [version 1; peer review: 1 approved with reservations] Sameera Senanayake https://orcid.org/0000-0002-5606-2046 1,2 , Adrian Barnett https://orcid.org/0000-0001-6339-0374 1 , David Brain https://orcid.org/0000-0002-6612-348X 1 , [...] Michelle Allen https://orcid.org/0000-0003-2178-4054 1 , Elizabeth E Powell 3-5 , James O’Beirne 6 , Patricia Valery 7 , Ingrid J Hickman 3,8 , Sanjeewa Kularatna 1,2 Sameera Senanayake https://orcid.org/0000-0002-5606-2046 1,2 , Adrian Barnett https://orcid.org/0000-0001-6339-0374 1 , [...] David Brain https://orcid.org/0000-0002-6612-348X 1 , Michelle Allen https://orcid.org/0000-0003-2178-4054 1 , Elizabeth E Powell 3-5 , James O’Beirne 6 , Patricia Valery 7 , Ingrid J Hickman 3,8 , Sanjeewa Kularatna 1,2 PUBLISHED 16 Jan 2025 Author details Author details 1 Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, Queensland University of Technology, Brisbane, Queensland, 4001, Australia 2 Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore 3 The University of Queensland, Saint Lucia, Queensland, 4072, Australia 4 Centre for Liver Disease Research, Translational Research Institute, The University of Queensland, Brisbane, Australia 5 Department of Gastroenterology and Hepatology, Princess Alexandra Hospital, Brisbane, Australia 6 University of the Sunshine Coast, Maroochydore DC, Queensland, Australia 7 QIMR Berghofer Medical Research Institute, Royal Brisbane Hospital, Herston, QLD, 4029, Australia 8 Department of Nutrition and Dietetics, Princess Alexandra Hospital, Brisbane, QLD, Australia Sameera Senanayake Roles: Conceptualization, Formal Analysis, Methodology, Validation, Visualization, Writing – Original Draft Preparation Adrian Barnett Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Writing – Review & Editing David Brain Roles: Conceptualization, Funding Acquisition, Resources, Writing – Original Draft Preparation Michelle Allen Roles: Investigation, Resources, Visualization, Writing – Review & Editing Elizabeth E Powell Roles: Conceptualization, Funding Acquisition, Investigation, Writing – Original Draft Preparation James O’Beirne Roles: Conceptualization, Funding Acquisition, Investigation, Writing – Review & Editing Patricia Valery Roles: Conceptualization, Funding Acquisition, Project Administration, Writing – Review & Editing Ingrid J Hickman Roles: Conceptualization, Funding Acquisition, Project Administration, Writing – Review & Editing Sanjeewa Kularatna Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Health Services gateway. Abstract Background Chronic diseases are a significant health concern in Australia. Understanding community preferences for health screening services is vital for enhancing service delivery. We conducted a study to determine community preferences for health screening services for chronic diseases in Australia using a discrete choice experiment (DCE). This paper aims to present the development of the final DCE design using priors estimated from a survey. Methods A DCE was conducted in Australia. An online survey was administered to a general Australian population over 18. The final attribute list of five attributes with three levels each was designed. A D-efficient design with 30 pair-wise choice tasks was developed using a fractional factorial design. A pre-test was conducted to assess comprehension and understanding of the online DCE survey. The pilot survey aimed to compute priors (i.e. coefficients) associated with attributes. A multinomial logit model was used to analyse the pilot DCE data. Results The survey included 30 choice tasks in three blocks, with 119 participants responding. The best DCE design was selected based on D-error, with a lower D-error indicating the most efficient design. The pilot survey results indicated a strong preference for highly accurate screening tests, with coefficients for 85% and 95% accuracy being positive. Coefficients estimated from the pilot survey were used as priors to design the DCE choice tasks for the main survey. The final DCE design showed a notable improvement in the attribute level overlap compared to the design used for the pilot survey. Conclusions A rigorous approach was taken to develop a DCE survey that could effectively determine the preferences of the community for health screening services. The resulting DCE design consisted of 30 choice tasks presented in pairs and was deemed efficient enough to gather comprehensive information in the final survey. READ ALL READ LESS Keywords Community screening, discrete choice experiment, D-efficient design Corresponding Author(s) Sameera Senanayake ( [email protected] ) Close Corresponding author: Sameera Senanayake Competing interests: No competing interests were disclosed. Grant information: National Health and Medical Research Council (NHMRC), Australia, has provided funding for this study (grant number 1175567). This funding source had no role in the design of this study and had no role during its execution, analyses, interpretation of the data, or decision to submit results. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Senanayake S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Senanayake S, Barnett A, Brain D et al. A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :96 ( https://doi.org/10.12688/f1000research.157017.1 ) First published: 16 Jan 2025, 14 :96 ( https://doi.org/10.12688/f1000research.157017.1 ) Latest published: 16 Jan 2025, 14 :96 ( https://doi.org/10.12688/f1000research.157017.1 ) Background According to the Australian Institute of Health and Welfare, chronic diseases such as heart disease, diabetes, cancer, and respiratory disease are the leading cause of illness, disability, and death in Australia, and their burden is projected to rise due to factors such as population aging, changing lifestyles, and environmental factors. 1 Community screening programs for chronic diseases need to be implemented to address this challenge. However, to ensure the success and sustainability of such programs, eliciting population preferences for the types of health screening services offered is crucial. Understanding the community’s preferences is critical in designing community screening programs that are both effective and well-received by the target population. 2 By doing so, these programs can be tailored to the specific needs and preferences of the community, increasing their acceptability, and improving their effectiveness in identifying and managing chronic diseases. Discrete choice experiments (DCEs) are gaining popularity in finding otherwise unavailable answers for problems in healthcare. The insights provided by DCEs into patients’ and healthcare workers’ preferences can help make decisions in health services and allocate scarce resources. DCEs being a stated preference method, facilitate decisions holding multiple trade-offs simultaneously and allow consideration of the relative importance of various attributes (and their levels) when making that decision. 3 This method allows insights into subtleties of decision-making within stakeholder groups, making it possible to explore the trade-offs between alternatives that sometimes are hypothetical. 4 For example, a DCE could simultaneously examine provision of care in hospital or a community setting, care provided by a nurse or specialist, acceptable waiting times, and acceptable out-of-pocket costs. 4 The theoretical basis of the DCEs originates from the random utility theory (RUT), which describes choices made by individuals using discrete sets of alternatives. 3 A utility function can be used to describe the preference for an alternative by an individual. It is assumed that the alternative with the highest utility (most preferred) will be chosen. This utility depends on the attributes of the alternative (e.g., wait time, travel distance) and the individual making the choice (e.g., age, sex) and the unobserved attributes the data collector may not be aware of (e.g., co-morbidities of the individual making a choice). The observed (collect data on) and unobserved attributes are represented in the utility function by explanatory and random variables, respectively. 3 The attributes and their levels are used for the experimental design, which develops hypothetical choice sets to be compared by the respondents. 5 The respondents attach a utility to alternatives based on their preference for attribute-level combinations. The choices made by the respondents are considered mutually exclusive (can choose only one option) and collectively exhaustive (all the options are available for decision-making). As such, all the information must be provided within the attributes and levels for valid choices. 5 The presentation of all relevant information within a choice set reduces the random effects of the models in question. 6 To reduce the randomness of the developed model, it is of upmost importance to use attributes of the greatest relevance to those completing the DCE. Some DCEs used in healthcare policy settings have failed to report rigorous methods employed to develop appropriate attributes for the alternatives. 7 In health policy settings, the attributes are the characteristics of the intervention (e.g., community screening programme for chronic diseases), and each attribute (e.g., place of screening, waiting time) is designated with levels (e.g. hospital, community clinic, one week, one month). Attribute selection to represent the characteristics of the intervention need to follow a rigorous methodology to ensure all vital information is presented for the choice experiment. 8 Attribute selection ideally uses mixed methods, including qualitative methods such as focus groups, interviews, quantitative prioritisation, and final determination using expert panels. The participants in this data collection should include relevant stakeholders, including patients, care providers and decision-makers to ensure the representation of all views. 8 , 9 An important part of the design process is where hypothetical alternatives are generated and combined to develop choice sets. By manipulating the design, the investigator can reduce the response burden, increase statistical efficiency, and construct a simpler DCE. A full factorial design containing all possible combinations of attributes may not be feasible due to too many resulting alternatives and choice sets. Therefore, a fractional factorial is often used. 3 This design should be orthogonal and balanced. In orthogonal designs, attributes are statistically independent of each other so that the participants’ preferences can be estimated for each attribute. Balanced designs have each attribute occurring equally within choice sets. Choice designs that are both orthogonal and balanced are called orthogonal arrays and are not universally available for all combinations as they are not feasible for alternatives with five or more attributes with two or more levels. When orthogonal arrays are not feasible, designs need to find efficiency by trading off orthogonality and balance. 3 Compared to orthogonal designs, efficient designs increase the precision of parameter estimates by reducing coefficient standard errors and allowing some limited correlation between attributes. Most designs use D-efficient designs (D-error (inverse)/D-efficiency/D-optimal) to achieve this efficiency and are recommended to maximise statistical efficiency and minimise the variability of parameter estimates. 3 , 5 D-efficiency ranges from 0% to 100% where 100% denotes the greatest statistical efficiency. Prior information about the parameters in the model is required for efficient designs. 10 Since these priors may not always be available for the design algorithm, investigators should look for subsequent D-efficient designs after pilot surveys. The efficient design can use fixed point estimates (prior design) or probability distribution (Bayesian). 10 It is preferable to pilot at least with very small priors with hypothetical directions for some parameters and estimate the models to learn the direction and magnitude of priors for the next D-efficient design. Sample size calculations for DCE studies are evolving. However, it is an important part of the research as a appropriate sample size assures sufficient statistical power to detect a difference in preferences. Investigators tend to maximise the sample size to avoid underpowering DCEs. 11 However, given limited budgets, online data collections and the notorious non-responsiveness of patients and clinicians within healthcare DCEs, it is not practical to overpower the sample in most cases. There are heuristic and parametric approaches to estimating sample sizes in DCEs. The disadvantages of these methods have been discussed elsewhere, and methods to estimate sample size have been proposed based on significance level, statistical power, model, priors and design. 11 This paper presents methods for determining preferences for community screening programmes for chronic disease in Australia. We previously presented the attribute development for the choice sets. Here, we present the designing, pre-tests, pilots, and use of priors to organise the final design and the data collection for the DCE. Methods This project was designed to elicit community preferences for health screening services for individuals with chronic diseases such as diabetes, cardiovascular and liver disease, using a discrete choice experiment. This paper describes the designing of the D-efficient design for the pilot survey (Step 2), the results of the pre-test (Step 3) and the pilot survey (Step 4) and designing of the final D-efficient design for the main DCE survey (Step 5) ( Figure 1 ). The final DCE design was developed after a pre-test and a pilot survey, and the methods and results are presented in later sections. The pilot survey (Step 4) was used to estimate the priors to develop the D-efficient design for the main DCE survey (Step 5). In developing this study and presenting the findings, we adhered to A Reporting Checklist for Discrete Choice Experiments in Health: The DIRECT Checklist, ensuring transparency and methodological rigour throughout the study process. 12 Figure 1. Steps for designing the final DCE choice tasks. Ethics approval for this study was granted by the Queensland University of Technology Human Research Ethics Committee (QUT HREC), with reference number HREC/QUT/4282, on 02/08/2021. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki, ensuring the protection of participants’ rights, safety, and well-being throughout the research process. Step 1: Development of attributes and levels The design was a non-labelled DCE study with respondents presented with two hypothetical scenarios (i.e., choice sets), each containing five attributes with three levels each, which individuals were asked to choose between. The selection of the final set of attributes and levels for this DCE was based on a systematic review of the literature, 7 focus groups with consumers and health service providers, a quantitative structured prioritisation exercise and an expert panel discussion to finalise the attributes and levels. 9 The final five attributes were: screening conduct, quality and accuracy of the test results, cost to the patient, wait time to appointment for screening, and source of information about the importance of screening and the screening process ( Table 1 ). Table 1. Attributes and attributes’ level for the discrete choice experiment. Attribute description Levels Screening conduct • Nurse at local community health clinic • General practinioner (GP) at your usual GP clinic • Specialist in hospital outpatient clinic Quality and accuracy of the test results • 75% accurate - For every 100 people who had a negative result, 25 would be incorrect and should have been positive • 85% accurate - For every 100 people who had a negative result, 15 would be incorrect and should have been positive • 95% accurate - For every 100 people who had a negative result, five would be incorrect and should have been positive Cost to the patient (includes out-of-pocket costs such as parking, as well as lost income for the time taken to undertake screening appointment) • $0 • $80 • $250 Wait time to appointment for screening • 2 weeks • 2 months • 6 months Source of information about the importance of screening and screening process • The screening information is detailed and comes from a well-trusted source, e.g. community member/health professional you have a good relationship with who discusses screening with you and provides a detailed flyer • Screening information comes from a source which you would have a moderate amount of trust, e.g. community member/health professional that you know moderately well quickly tells you that you need to be screened and hands you a short flyer • The screening information is sent to you from a source where you have limited familiarity or trust, e.g. you receive a generic text, email or letter about screening Step 2: Designing DCE choice tasks – pilot survey The final attribute list had five attributes with three levels each, and that would result in 59,049 (3 10 ) possible choice tasks. An example choice task is given in Figure 2 . Since it is not feasible to present all possible combinations (n=59,049) to all the respondents, 30 choice tasks were selected using Ngene version 1.3.0 software, in a fractional factorial design. The 30 choice tasks make up the choice set. The main aim of using a fractional factorial design was to have a manageable number of choice tasks while maximising the design’s statistical efficiency. 13 Therefore, a multinomial logit model based on D-efficient fractional factorial design criteria (using the D-error value) was used to develop 30 pair-wise choice tasks using the design software Ngene. Evidence indicates that respondents can efficiently handle ten choice sets at a time. 8 , 14 Therefore, the fractional factorial design was divided into three blocks so that a respondent would only answer ten from the 30 choice tasks in the fractional factorial design. Blocking is an accepted statistical technique in a DCE design that ensures an equal number of respondents per block. 5 We used the modified Federov algorithm to develop the D-efficient design, which is known to develop designs with attribute level balance and no dominant choice tasks. 15 Figure 2. Example of a choice set seen by respondents. In the absence of prior information on the coefficients of the different attributes, small positive or negative priors or zero priors (non-informative priors) were used to design the D-efficient design based on the following a priori hypotheses ( Table 2 ). • People have equal preference for a nurse-led, general practitioner-led, and specialist-led screening programme. Therefore, we used non-informative (zero) priors. • People have a preference for quality and accurate screening tests. • People have a negative preference for out-of-pocket cost and wait-time. • People do not have a strong preference for different sources of information. Therefore, we used non-informative (zero) priors. Table 2. Estimates used to design the choice tasks for the pilot and the final survey. Attribute Priors used in the Ngene design DCE design used for the pilot survey DCE design used for the final survey Screening conduct Nurse at local community health clinic 0 Reference General practinioner at your usual GP clinic 0 0.89071 Specialist in hospital outpatient clinic Reference 0.44991 Quality and accuracy of the test results 75% accurate 0.000001 (Continuous scale) Reference 85% accurate 0.97828 95% accurate 1.02893 Cost to the patient (per $100) – levels $0, $80, $250 -0.000001 -0.01173 Wait time (per week) – Levels 2, 8, 24 weeks -0.000001 -0.50724 Source of information Well trusted source 0 Reference Moderate amount of trust 0 0 Limited familiarity or trust Reference -1.10438 Measures of efficiency D-error 0.0083 0.0585 S estimate 7.434 Using moderately informative priors for two of the attributes means the 30 choices lean towards those with larger differences in the other attributes, so more information is gathered on these attributes. Apart from the D-error, attribute level overlap and attribute level balance were used to assess the DCE design. Attribute level overlap is when the same level is present in both choice tasks, essentially eliminating this attribute from that choice. Attribute level balance is the distribution of the attribute levels across the two choice tasks. Lower attribute level overlap and equal distribution of levels indicate a better DCE design. Step 3: Pre-test The pre-test aimed to ascertain comprehension and understanding of the online DCE survey. The online survey was administered to a sample of 10 members of the general population. Empirical studies have shown that a sample size of ten respondents is sufficient for checking for readability and clarity before use in a broader population. 16 , 17 The web-based DCE survey contained three parts. Respondents were first given information and instructions on completing the DCE and shown a sample task. They were also required to provide consent to continue the survey. Demographic data were collected (e.g., gender, age, education) to summarise the characteristics of the study participants. The second part contained the ten DCE tasks. In addition to these ten choice tasks per respondent, a repeated choice task and a dominant choice task were also included to assess the internal reliability and consistency of responses, creating 12 DCE choice tasks presented to each participant. A choice task with an apparent dominant option was presented at the beginning of the main DCE tasks. The proportion who got this dominant option correct was considered a proxy indicator of the internal reliability and consistency of responses. The third-choice task was repeated at the end of the ten main tasks. The proportion who got the same answer to the repeated tasks was also considered a proxy indicator of the internal reliability and consistency of responses. In the third component, respondents were asked to rate their difficulty completing DCE tasks, including their ability to understand the words used in the survey tool and ease of following the instructions. The time a respondent took to complete the survey tool was also recorded. Step 4: Pilot survey The pilot survey aimed to compute the priors and the best estimation coefficients associated with attributes, so that a more efficient DCE design could be developed. An online survey was administered to a representative sample of the general Australian population over 18 years of age. The data were collected in March 2022. 18 Eligible respondents were sourced from the online survey panel PureProfile, an Australian online survey panel ( https://www.pureprofile.com/ ). Pureprofile survey panels have been successfully used in population-based surveys in Australia. 19 , 20 The respondents were drawn from participants who have subscribed to the PureProfile website for the purposes of completing surveys. A multinomial logit model under a random utility framework was used to analyse the pilot DCE data. The random utility framework assumes that the participants chose the alternative that maximised their utility. The utility function is estimated using the five program attributes and a random error term. The analysis was conducted in NLOGIT 5 software ( https://www.limdep.com/products/nlogit ). Step 5: Designing DCE choice tasks - Main survey Similar to step 2, 30 choice tasks (three blocks with ten choice tasks each) were selected using Ngene software, using the multinomial logit model-based D-efficient fractional factorial design criteria. The coefficients (priors) of the pilot survey were used to improve the statistical efficiency of the final experimental DCE choice tasks design. Sample size calculation for healthcare DCE studies is a developing field. 21 A minimum required sample size for a parameter can also be calculated, once reliable priors are obtained. 22 , 23 Assuming the prior coefficient is β 1 and the standard error is SE 1, the following equation would give the minimum required sample size a parameter (e.g. β1) can be estimated at 95% statistically significant level. 11 ( 1.96 × SE 1 β 1 ) 2 Ngene software calculates this parameter (S-estimate) and this indicates the smallest sample size needed for all the parameters to be statistically significant. 11 Furthermore, as in step 2, attribute level overlap and balance were also assessed. Results Step 2: Designing DCE choice tasks – pilot survey Based on our a priori hypotheses, the priors used to design the pilot survey are listed in Table 2 . Quality and accuracy of the test results, cost to the patient, and wait time were continuous variables, while the others were categorical. The best DCE design was selected based on D-error, with the lowest D-error indicating the most efficient design. The Ngene software was run for around 20 hours. A further description of the design used for the pilot study is presented in supplementary table 1. 24 Of the 30 choice tasks, the quality and accuracy of the test results (5/30), cost to the patient (9/30), and wait time (9/30) had overlapping attribute levels. The three levels of the five attributes were almost equally distributed in both the choice tasks. Step 3: Pre-test The main aim of the pre-test was to assess the face validity of the online survey (including the feasibility and appropriateness of the number of attributes in a choice task). The practical difficulties arising while completing the online survey were also assessed. The average time taken to complete the questionnaire was approximately 10 minutes. Based on the survey results, a few modifications were made to the wording of some instructions. Step 4: Pilot survey A total of 119 participants responded to the survey, and Table 3 describes the sample’s demographic characteristics. More than three-quarters of the sample were less than 55 years, and there were more males (67%). The majority were residing in metropolitan areas (71%). Only 37% had ever attended a health screening programme. Table 3. Sample characteristics. Variable Categories Number (%)N=119 Age in years 18-35 39 (33) 36-55 51 (43) 56-75 23 (19) 75+ 6 (5) Sex Male 39 (33) Female 80 (67) Area of residence Metropolitan City 85 (71) Regional 34 (29) Level of education Grade 10 8 (7) Grade 12 15 (13) Diploma 15 (13) Certificate II-IV 27 (23) Bachelor 37 (31) Masters/PhD 15 (13) Other 2 (2) % who have attended a health screening program 44 (37) The average time taken to respond to the survey was 10 minutes (inter quartile rage 5 mins to 11 mins), which was within the expected average time according to the pre-test. The dominant and repeat tasks were correct in 95% and 90% of the responses, respectively. Eighty-one percent (81%) indicated that they did not find it difficult to understand these tasks. Every individual who participated in the survey completed it in full. Table 4 reports estimates for the multinomial logit model. The highest utility was for a highly accurate screening test (1.03), and the lowest was for wait-time (-0.51). The coefficients for screening provided by either the GP at their regular GP clinic or by a specialist in a hospital outpatient clinic were positive indicating that respondents preferred to be screened by these providers than by a local community health clinic nurse. There was a strong preference for a highly accurate screening test, indicated by the positive utility for 85% and 95% accurate screening tests. There was a disutility when the source of information was limited familiarity and trust (-1.10). Table 4. Model estimates of the pilot survey. Pilot study Coefficient (95% confidence interval) Constant 1.94 (1.32 to 2.55) Screening conduct Nurse at local community health clinic Reference GP at your usual GP clinic 0.89 (0.44 to 1.33) Specialist in hospital outpatient clinic 0.45 (0.02 to 0.87) Quality and accuracy of the test results 75% accurate Reference 85% accurate 0.98 (0.57 to 1.4) 95% accurate 1.03 (0.65 to 1.4) Cost to the patient (per $100) -0.01 (-0.013 to -0.01) Wait time (per week) -0.51 (-0.57 to -0.43) Source of information Well trusted source Reference Moderate amount of trust -0.15 (-0.54 to 0.25) Limited familiarity or trust -1.10 (-1.54 to -0.66) Step 5: Designing DCE choice tasks - Main survey Coefficients estimated from the pilot survey were used as priors to design the DCE choice tasks for the main DCE survey ( Table 2 ). The Ngene software was run for around 24 hours; the S-estimate was 7.434. Results of the attribute level overlap and attribute level balance are presented in Table 3 . There was a notable improvement in the attribute level overlap in the main DCE choice tasks compared to the DCE choice tasks used for the pilot survey. Of the 30 choice tasks, only cost to the patient (4/30), and wait time (4/30) had overlapping attribute levels. The three levels of the five attributes were almost equally distributed in both the choice tasks. Discussion The main aim of this project was to design a DCE choice set that could elicit community preferences for health screening services for individuals with chronic diseases such as diabetes, cardiovascular and liver disease. The final set of attributes and levels for the DCE was based on a systematic review of the literature, 7 qualitative interviews, a quantitative structured prioritisation exercise and an expert panel discussion. 9 We followed a robust methodology to develop an efficient choice set that captures maximum information. The final choice set had 30 pair-wise choice tasks divided into three blocks, with minimum attribute level overlap and satisfactory attribute level balance. Our study used the D-efficient criterion to design a fractional factorial design with 30 pair-wise choice tasks. The D-efficient criterion is probably the most common efficiency criterion in designing DCE choice tasks. 25 The number of pair-wise choice tasks (rows) in the design depends on the number of parameters in the utility specification. The minimum number of pair-wise choice tasks (rows) of the DCE design equals to or greater than the number of parameters, not including constants, plus one. 26 Our study had eight parameters, indicating that the minimum number of choice tasks would be nine. However, the number of choice tasks is often set to at least two or three times the minimum size to have sufficient degrees of freedom. Therefore, the use of 30 pair-wise choice tasks in the current DCE design would provide enough variation in the design matrix to estimate reliable parameter coefficients in the final DCE survey. Our study used a heterogenous design, meaning each respondent responded only to a subset of the choice tasks. The choice set was divided into three blocks so that each respondent answered only ten choice tasks to reduce the burden on participants of answering all the choice tasks. Heterogeneous designs are generally considered better as they provide more information than homogenous designs. 27 The number of choice tasks each respondent receives depends on the complexity of each choice task and how many the analysts believe a respondent can handle without fatigue. Mixed evidence exists as to the impact the number of choice tasks has empirically upon choice experiments. Hensher et al. suggested using 4 to 16 choice tasks 28 ; however, few studies indicate that the number of choice tasks each respondent sees has the least influence on the error variance of choice data. 29 , 30 Our DCE design had only five attributes, and an expert panel validated the attributes and the levels not to be mentally demanding when put into a choice set. Furthermore, based on the completion rate, the pre-test indicated that a respondent could handle ten choice tasks without any fatigue. Efficient designs have the potential to select a subset of choice tasks from the full factorial design that yields more information, estimate smaller standard errors and increase the reliability of the parameter estimates. 26 However, it is important to note that the efficiency of the design depends on the prior parameter estimates used in the model. If the priors are incorrect or close to actual behaviours, the design can become inefficient, leading to larger standard errors. 31 Since no prior estimates were available in the literature, we conducted a pilot study to estimate the priors. This step has been recommended and could significantly improve the quality of the information in the final DCE survey through smart choice tasks with appropriate trade-offs across the attributes. 22 , 32 This means that the final DCE survey designed in this study can potentially estimate reliable parameter estimates at smaller sample sizes. However, several systematic reviews which have reviewed DCE studies report that most studies either fail to report the source of the priors or use non-informative (zero) or conservative (close to zero) priors for the DCE design. 7 , 33 This is a critical drawback as this limits the ability for critical appraisal and reproducibility of the survey. Furthermore, this leads to inefficient DCE designs that may require larger sample sizes to collect the same amount of information compared to a more efficient design. The two DCE designs (for the pilot and the main study) developed in the study used constraints at the design stage to achieve attribute level balance, and the results indicate that the two designs achieved a satisfactory level of attribute balance. Imposing attribute balance constraints could have reduced the efficiency of the DCE design. 26 However, some degree of attribute level balance in the design ensured that all parameter levels were represented. This would ensure that the parameter coefficients in the main DCE survey could be estimated well on the whole range of levels instead of having data points at only one or a few attribute levels. Limitation We used fixed priors in the multinomial logit model to design the efficient fractional factorial design. Informative Bayesian priors have been proposed to produce more robust DCE designs against prior misspecification. 34 However, this comes at a high computational cost and may not be feasible. Furthermore, it is common practice to design the DCE choice set using a fixed priors, and evidence indicates that this method works well even for estimating parameter coefficients of a panel mixed logit model. 35 Conclusion We followed a robust methodology to design a DCE choice set that could elicit community preferences for health screening services. The final DCE design had 30 pair-wise choice tasks and demonstrated satisfactory efficiency that will capture maximum information and best inform policy. Declarations Ethics approval and consent to participate Ethics approval for this study was granted by the Queensland University of Technology Human Research Ethics Committee (QUT HREC), with reference number HREC/QUT/4282, on 02/08/2021. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki, ensuring the protection of participants’ rights, safety, and well-being throughout the research process. Informed consent was obtained from all participants before their involvement in the study. On the first page of the online survey, participants were required to review the participant information sheet detailing the study’s purpose, procedures, potential risks, and benefits. Participants indicated their intention to participate by clicking “Agree” or opted not to participate by clicking “Not Agree” in the online consent form. Only those who selected “Agree” were redirected to the survey tool. Due to the nature of the online recruitment method, consent was collected electronically rather than through written signatures. This approach was approved by the Queensland University of Technology Human Research Ethics Committee (reference number HREC/QUT/4282). The ethics committee deemed this method appropriate as participants were provided with sufficient information to make an informed decision and had the opportunity to review the participant information sheet in full before providing their consent. Consent for publication Not applicable. Authors’ contributions SK, SS, AB, and DB contributed to the design of the study, coordinated the collection of data, analysed the data, and drafted the manuscript. MA, EEP, JO’B, PV, and IH contributed to the development of the data analysis plan, interpretation of the results, and review of the manuscript. All authors have read and approved the final version of the manuscript. Availability of data and materials Figshare: A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey. https://doi.org/10.6084/m9.figshare.27644715 . 18 Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Extended data Figshare: A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey. The project contains the following extended data: • Supplementary file: DOI: https://doi.org/10.6084/m9.figshare.28079702 . 24 Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Software availability Ngene version 1.3.0: • Source code available from: GitHub Repository ( https://github.com/agbarnett/LOCATE/blob/master/NGene%20Codes.txt ) • Archived software available from: Not applicable (commercial software). • License: Proprietary software. • Website: https://www.choice-metrics.com/ . NLOGIT version 5: • Source code available from: GitHub Repository ( https://github.com/agbarnett/LOCATE/blob/master/Nlogit%20codes.txt ) • Archived software available from: Not applicable (commercial software). • License: Proprietary software. • Website: https://www.limdep.com/products/nlogit/ . Acknowledgements The study team would like to acknowledge Ruth Tulleners for her contribution to the project management of the study, critical review of project documentation, and coordination the ethics process. References 1. Australian Institute of Health and Welfare: Chronic disease - Overview. Australian Institute of Health and Welfare; 2022. Reference Source 2. Baker DW, Brown T, Buchanan DR, et al. : Comparative effectiveness of a multifaceted intervention to improve adherence to annual colorectal cancer screening in community health centers: a randomized clinical trial. JAMA Intern. Med. 2014; 174 (8): 1235–1241. PubMed Abstract | Publisher Full Text 3. Lancsar E, Louviere J: Conducting discrete choice experiments to inform healthcare decision making: a user’s guide. PharmacoEconomics. 2008; 26 (8): 661–677. Publisher Full Text 4. Clark MD, Determann D, Petrou S, et al. : Discrete choice experiments in health economics: a review of the literature. PharmacoEconomics. 2014; 32 (9): 883–902. Publisher Full Text 5. Johnson FR, Lancsar E, Marshall D, et al. : Constructing experimental designs for discrete-choice experiments: report of the ISPOR conjoint analysis experimental design good research practices task force. Value Health. 2013; 16 (1): 3–13. PubMed Abstract | Publisher Full Text 6. Mangham LJ, Hanson K, McPake B: How to do (or not to do) … Designing a discrete choice experiment for application in a low-income country. Health Policy Plan. 2009; 24 (2): 151–158. PubMed Abstract | Publisher Full Text 7. Brain D, Jadambaa A, Kularatna S: Methodology to derive preference for health screening programmes using discrete choice experiments: a scoping review. BMC Health Serv. Res. 2022; 22 (1): 1079. PubMed Abstract | Publisher Full Text | Free Full Text 8. De Brún A, Flynn D, Ternent L, et al. : A novel design process for selection of attributes for inclusion in discrete choice experiments: case study exploring variation in clinical decision-making about thrombolysis in the treatment of acute ischaemic stroke. BMC Health Serv. Res. 2018; 18 (1): 483. PubMed Abstract | Publisher Full Text | Free Full Text 9. Allen MJ, Doran R, Brain D, et al. : A discrete choice experiment to elicit preferences for a liver screening programme in Queensland, Australia: a mixed methods study to select attributes and levels. BMC Health Serv. Res. 2023; 23 (1): 1–12. 10. Szinay D, Cameron R, Naughton F, et al. : Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design. J. Med. Internet Res. 2021; 23 (10): e32365. PubMed Abstract | Publisher Full Text | Free Full Text 11. Rose JM, Bliemer MCJ: Sample size requirements for stated choice experiments. Transportation. 2013; 40 (5): 1021–1041. Publisher Full Text 12. Ride J, Goranitis I, Meng Y, et al. : A Reporting Checklist for Discrete Choice Experiments in Health: The DIRECT Checklist. PharmacoEconomics. 2024; 42 (10): 1161–1175. PubMed Abstract | Publisher Full Text | Free Full Text 13. Oedingen C, Bartling T, Krauth C: Public, medical professionals’ and patients’ preferences for the allocation of donor organs for transplantation: study protocol for discrete choice experiments. BMJ Open. 2018; 8 (10): e026040. PubMed Abstract | Publisher Full Text | Free Full Text 14. Netten A, Burge P, Malley J, et al. : Outcomes of social care for adults: developing a preference-weighted measure. Health Technol. Assess. 2012; 16 (16): 1–166. PubMed Abstract | Publisher Full Text 15. Cook RD, Nachtrheim CJ: A comparison of algorithms for constructing exact D-optimal designs. Technometrics. 1980; 22 (3): 315–324. Publisher Full Text 16. Howard K, Salkeld GP, Patel MI, et al. : Men’s preferences and trade-offs for prostate cancer screening: a discrete choice experiment. Health Expect. 2015; 18 (6): 3123–3135. PubMed Abstract | Publisher Full Text | Free Full Text 17. Mansfield C, Ekwueme DU, Tangka FKL, et al. : Colorectal Cancer Screening: Preferences, Past Behavior, and Future Intentions. Patient. 2018; 11 (6): 599–611. PubMed Abstract | Publisher Full Text | Free Full Text 18. A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey. Figshare. 2024. Publisher Full Text 19. Rahja M, Laver K: What does the Australian public know about occupational therapy for older people? A population survey. Aust. Occup. Ther. J. 2019; 66 (4): 511–518. PubMed Abstract | Publisher Full Text 20. Rahja M, Laver K, Comans T, et al. : What Does the Australian General Public Know About Treatments for Dementia? A Population Survey. Gerontol. Geriatr. Med. 2018; 4 : 2333721418793442. PubMed Abstract | Publisher Full Text | Free Full Text 21. Wong SF, Norman R, Dunning TL, et al. : A protocol for a discrete choice experiment: understanding preferences of patients with cancer towards their cancer care across metropolitan and rural regions in Australia. BMJ Open. 2014; 4 (10): e006661. PubMed Abstract | Publisher Full Text | Free Full Text 22. Bliemer MC, Collins AT: On determining priors for the generation of efficient stated choice experimental designs. J. Choice Model. 2016; 21 : 10–14. Publisher Full Text 23. de Bekker-Grob EW , Donkers B, Jonker MF, et al. : Sample size requirements for discrete-choice experiments in healthcare: a practical guide. Patient. 2015; 8 : 373–384. PubMed Abstract | Publisher Full Text | Free Full Text 24. A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey. Figshare. 2024. Publisher Full Text 25. Ozdemir S, Lee JJ, Chaudhry I, et al. : A systematic review of discrete choice experiments and conjoint analysis on genetic testing. Patient. 2021; 15 : 39–54. Publisher Full Text 26. Rose JM, Bliemer MC: Constructing efficient stated choice experimental designs. Transp. Rev. 2009; 29 (5): 587–617. Publisher Full Text 27. Sándor Z, Wedel M: Heterogeneous conjoint choice designs. J. Mark. Res. 2005; 42 (2): 210–218. Publisher Full Text 28. Hensher DA, Stopher PR, Louviere JJ: An exploratory analysis of the effect of numbers of choice sets in designed choice experiments: an airline choice application. J. Air Transp. Manag. 2001; 7 (6): 373–379. Publisher Full Text 29. Bech M, Kjaer T, Lauridsen J: Does the number of choice sets matter? Results from a web survey applying a discrete choice experiment. Health Econ. 2011; 20 (3): 273–286. PubMed Abstract | Publisher Full Text 30. Rose JM, Hensher DA, Caussade S, et al. : Identifying differences in willingness to pay due to dimensionality in stated choice experiments: a cross country analysis. J. Transp. Geogr. 2009; 17 (1): 21–29. Publisher Full Text 31. Bliemer MC, Rose JM, Chorus CG: Detecting dominance in stated choice data and accounting for dominance-based scale differences in logit models. Transp. Res. B Methodol. 2017; 102 : 83–104. Publisher Full Text 32. de Bekker-Grob EW , Ryan M, Gerard K: Discrete choice experiments in health economics: a review of the literature. Health Econ. 2012; 21 (2): 145–172. Publisher Full Text 33. Vass C, Gray E, Payne K: Discrete choice experiments of pharmacy services: a systematic review. Int. J. Clin. Pharm. 2016; 38 (3): 620–630. PubMed Abstract | Publisher Full Text 34. Sandor Z, Wedel M: Designing conjoint choice experiments using managers’ prior beliefs. J. Mark. Res. 2001; 38 (4): 430–444. Publisher Full Text 35. Bliemer MC, Rose JM: Construction of experimental designs for mixed logit models allowing for correlation across choice observations. Transp. Res. B Methodol. 2010; 44 (6): 720–734. Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 16 Jan 2025 ADD YOUR COMMENT Comment Author details Author details 1 Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, Queensland University of Technology, Brisbane, Queensland, 4001, Australia 2 Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore 3 The University of Queensland, Saint Lucia, Queensland, 4072, Australia 4 Centre for Liver Disease Research, Translational Research Institute, The University of Queensland, Brisbane, Australia 5 Department of Gastroenterology and Hepatology, Princess Alexandra Hospital, Brisbane, Australia 6 University of the Sunshine Coast, Maroochydore DC, Queensland, Australia 7 QIMR Berghofer Medical Research Institute, Royal Brisbane Hospital, Herston, QLD, 4029, Australia 8 Department of Nutrition and Dietetics, Princess Alexandra Hospital, Brisbane, QLD, Australia Sameera Senanayake Roles: Conceptualization, Formal Analysis, Methodology, Validation, Visualization, Writing – Original Draft Preparation Adrian Barnett Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Writing – Review & Editing David Brain Roles: Conceptualization, Funding Acquisition, Resources, Writing – Original Draft Preparation Michelle Allen Roles: Investigation, Resources, Visualization, Writing – Review & Editing Elizabeth E Powell Roles: Conceptualization, Funding Acquisition, Investigation, Writing – Original Draft Preparation James O’Beirne Roles: Conceptualization, Funding Acquisition, Investigation, Writing – Review & Editing Patricia Valery Roles: Conceptualization, Funding Acquisition, Project Administration, Writing – Review & Editing Ingrid J Hickman Roles: Conceptualization, Funding Acquisition, Project Administration, Writing – Review & Editing Sanjeewa Kularatna Roles: Conceptualization, Funding Acquisition, Methodology, Supervision, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information National Health and Medical Research Council (NHMRC), Australia, has provided funding for this study (grant number 1175567). This funding source had no role in the design of this study and had no role during its execution, analyses, interpretation of the data, or decision to submit results. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 16 Jan 2025, 14:96 https://doi.org/10.12688/f1000research.157017.1 Copyright © 2025 Senanayake S et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Senanayake S, Barnett A, Brain D et al. A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :96 ( https://doi.org/10.12688/f1000research.157017.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 16 Jan 2025 Views 0 Cite How to cite this report: Zimba R. Reviewer Report For: A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :96 ( https://doi.org/10.5256/f1000research.172410.r365931 ) The direct URL for this report is: https://f1000research.com/articles/14-96/v1#referee-response-365931 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 11 Mar 2025 Rebecca Zimba , The CUNY Graduate School of Public Health & Health Policy (CUNY SPH), Harlem, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.172410.r365931 Thank you for the opportunity to review this article, which demonstrates thorough understanding of discrete choice experiment design considerations, but doesn't report actionable findings. 'I believe this article should be supplemental material to an article that describes the findings from ... Continue reading READ ALL Thank you for the opportunity to review this article, which demonstrates thorough understanding of discrete choice experiment design considerations, but doesn't report actionable findings. 'I believe this article should be supplemental material to an article that describes the findings from their main survey. Whether the authors decide to pursue publication of this material as a stand-alone article or include it as supplemental material to a different manuscript, I believe my suggestions will further strengthen the end product. Major concerns The main outcome from this paper appears to be the computation of point-estimate priors with which to refine the statistical efficiency of the final design, and as such, it may not warrant a full publication. That said, the manuscript as written is very heavy on describing methods - almost like a protocol paper - and I would encourage the authors to revise the title of this manuscript accordingly. One suggestion: "Pilot of a discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland Australia." I would omit "designing the choice sets for the final survey," because the biggest impact on the final survey was how the priors identified in the pilot improved the statistical efficiency of its design. The rest of the design (attributes, levels, choice sets, block design) had already been established in the pilot study and was not revised for the final survey. Please review the abstract and manuscript and, where appropriate, distinguish more consistently between the pilot survey and the final survey. For instance, in the Abstract Methods section, it seems like the authors are talking about the final survey ("A DCE was conducted in Australia") but this paper only reports on the pilot study. I believe the uses of "choice task" and "choice set" could be improved. In practice I believe these two terms are often used synonymously, though "choice task" includes the question wording and any instructions along with the choice set, whereas "choice set" includes only the two alternatives comprised of the attributes and levels. The authors may wish to preserve the distinction between "choice task" and "choice set", and if so they should define these terms at the outset of the methods and use them consistently throughout. Notably, the word "alternative" helpfully appears in the Background of the manuscript, but is only used once in the Methods. I believe the manuscript could be improved by a review of the use of these terms. I have identified places for improvement below, but the authors are encouraged to search for and review all uses of "choice task" and "choice set." In the Abstract Conclusions section, the second sentence is imprecise - it currently reads "the resulting DCE design consisted of 30 choice tasks presented in pairs..." which makes it seem as though two choice sets (with an unknown number of alternatives within each set) are presented at the same time. Consider revising to "the resulting DCE design consisted of 30 choice sets with two alternatives each..." In Methods, Step 2 paragraph, five attributes with three levels each would yield 3^5 (243) possible choice *alternatives*, not 3^10 (59,049) choice *tasks/sets*. Taking those alternatives to generate choice sets with two alternatives each would yield (3^5 * [3^5 - 1])/2 (29,403) choice sets. In Methods, Step 2 paragraph, "The 30 choice tasks make up the choice set." I believe this is confusing given my working definitions as stated above. I would omit this sentence as the previous sentence already states 30 choice tasks were selected. I would additionally revise the previous sentence to state that "30 choice sets were selected" (unless the authors decide to use choice set and choice task interchangeably). In Methods, Step 1 paragraph, the first sentence is confusing. I believe the parenthetical "(i.e. choice sets)" should be "(i.e. choice alternatives)." The phrase "each containing five attributes with three levels each" could be clarified if modified to "each containing five attributes varying across three levels each," or a similar modification to indicate that each choice alternative contains one level per attribute. I believe additional information about the development of the DCE attributes and levels should be included in Methods, Step 1. Though already described in a separate manuscript, some of the information may be summarized here. For instance, the current manuscript indicates that this DCE could be used to estimate community member preferences for chronic disease screening, but the mixed methods paper focused only on liver disease screening. Since there are many chronic disease conditions, and preferences for screening may vary depending on the condition, the authors may want to spend some time justifying their belief that these attributes and levels apply across conditions. While perhaps not relevant to the current manuscript, which focuses on the statistical design rather than the design of attributes and levels, a limitation of this DCE is that two of the attributes contain more than one construct. Screening conduct combines person and place, and while Source of information seems to focus on trust, the examples provided also include familiarity, the amount of time it takes for the information to be conveyed, the amount of detail conveyed, and the mode of communication, though not all of the levels' examples contain all of these constructs. Moreover, for both attributes, the sets of levels do not contain all combinations of construct elements (e.g. Nurse at your usual GP clinic is not included). Certainly all combinations of elements might not make sense, but this should be stated more explicitly. By including multiple constructs in one attribute, the authors are prevented from understanding the true elements within each level that influenced respondents' choices. Relatedly, I think the authors should justify why they chose to focus on negative predictive value in their "Quality and accuracy of the test results" attribute. Was there something in the focus groups or literature review that indicated negative predictive value was more important? Isn't positive predictive value equally important to community members? For instance, if positive predictive value is lower, then people will be diagnosed with a chronic condition though they don't truly have that condition. They may undergo unnecessary additional tests or initiate unnecessary treatment. They may experience unnecessary emotional distress. I also wonder what part of this attribute pertains to "Quality." For clarity, I recommend including footnotes in Table 2 and Table 4 to indicate which levels are continuous. The text notes that Quality and accuracy, Cost, and Wait time are all continuous, but currently only the Quality and accuracy attribute has a note about scale in Table 2, and those levels are listed as discrete as if it were a categorical attribute in Table 2 and Table 4. To avoid confusion, I would recommend reporting on these variables consistently. If the authors wish to provide estimates of the coefficients for results that are 85% and 95% accurate they can report those in-line, while reporting the per percentage point coefficient as for the other two continuous attributes. Minor concerns The citations for the Background paragraph and Methods paragraph on sample size calculations are from 2013 and 2014, respectively. If sample size calculation methods are truly evolving, one would expect more citations, and for most of them to be more recent than then. The first paragraph of the Methods does not include Step 1 in the list of steps, though it is included in Figure 1 and the next paragraph heading is "Step 1: Development of attributes and levels." The term S-estimate is described in the Methods but the S-estimate result from Table 2 is not interpreted in the Results. Does the S-estimate for the final design in Table 2 mean only 7.434 people need to take the DCE? Why do the reference levels change in Table 2 from the pilot survey to the final survey? Table 3 is inaccurately referenced in the Results, Step 5 paragraph. I believe the reference should be to Supplemental Table 1. Table 4 reports the coefficients from the pilot study. Apart from the confidence intervals and the constant, these are already reported in Table 2, as the "DCE design used for the final survey" column. Consider how to present this information in a way that avoids redundancy. The authors state that they used The DIRECT Checklist but they did not include the completed checklist in supplemental materials. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Infectious disease epidemiology and discrete choice experiments. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Zimba R. Reviewer Report For: A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :96 ( https://doi.org/10.5256/f1000research.172410.r365931 ) The direct URL for this report is: https://f1000research.com/articles/14-96/v1#referee-response-365931 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 16 Jan 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 Version 1 16 Jan 25 read Rebecca Zimba , The CUNY Graduate School of Public Health & Health Policy (CUNY SPH), Harlem, USA Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Zimba R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 11 Mar 2025 | for Version 1 Rebecca Zimba , The CUNY Graduate School of Public Health & Health Policy (CUNY SPH), Harlem, USA 0 Views copyright © 2025 Zimba R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Thank you for the opportunity to review this article, which demonstrates thorough understanding of discrete choice experiment design considerations, but doesn't report actionable findings. 'I believe this article should be supplemental material to an article that describes the findings from their main survey. Whether the authors decide to pursue publication of this material as a stand-alone article or include it as supplemental material to a different manuscript, I believe my suggestions will further strengthen the end product. Major concerns The main outcome from this paper appears to be the computation of point-estimate priors with which to refine the statistical efficiency of the final design, and as such, it may not warrant a full publication. That said, the manuscript as written is very heavy on describing methods - almost like a protocol paper - and I would encourage the authors to revise the title of this manuscript accordingly. One suggestion: "Pilot of a discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland Australia." I would omit "designing the choice sets for the final survey," because the biggest impact on the final survey was how the priors identified in the pilot improved the statistical efficiency of its design. The rest of the design (attributes, levels, choice sets, block design) had already been established in the pilot study and was not revised for the final survey. Please review the abstract and manuscript and, where appropriate, distinguish more consistently between the pilot survey and the final survey. For instance, in the Abstract Methods section, it seems like the authors are talking about the final survey ("A DCE was conducted in Australia") but this paper only reports on the pilot study. I believe the uses of "choice task" and "choice set" could be improved. In practice I believe these two terms are often used synonymously, though "choice task" includes the question wording and any instructions along with the choice set, whereas "choice set" includes only the two alternatives comprised of the attributes and levels. The authors may wish to preserve the distinction between "choice task" and "choice set", and if so they should define these terms at the outset of the methods and use them consistently throughout. Notably, the word "alternative" helpfully appears in the Background of the manuscript, but is only used once in the Methods. I believe the manuscript could be improved by a review of the use of these terms. I have identified places for improvement below, but the authors are encouraged to search for and review all uses of "choice task" and "choice set." In the Abstract Conclusions section, the second sentence is imprecise - it currently reads "the resulting DCE design consisted of 30 choice tasks presented in pairs..." which makes it seem as though two choice sets (with an unknown number of alternatives within each set) are presented at the same time. Consider revising to "the resulting DCE design consisted of 30 choice sets with two alternatives each..." In Methods, Step 2 paragraph, five attributes with three levels each would yield 3^5 (243) possible choice *alternatives*, not 3^10 (59,049) choice *tasks/sets*. Taking those alternatives to generate choice sets with two alternatives each would yield (3^5 * [3^5 - 1])/2 (29,403) choice sets. In Methods, Step 2 paragraph, "The 30 choice tasks make up the choice set." I believe this is confusing given my working definitions as stated above. I would omit this sentence as the previous sentence already states 30 choice tasks were selected. I would additionally revise the previous sentence to state that "30 choice sets were selected" (unless the authors decide to use choice set and choice task interchangeably). In Methods, Step 1 paragraph, the first sentence is confusing. I believe the parenthetical "(i.e. choice sets)" should be "(i.e. choice alternatives)." The phrase "each containing five attributes with three levels each" could be clarified if modified to "each containing five attributes varying across three levels each," or a similar modification to indicate that each choice alternative contains one level per attribute. I believe additional information about the development of the DCE attributes and levels should be included in Methods, Step 1. Though already described in a separate manuscript, some of the information may be summarized here. For instance, the current manuscript indicates that this DCE could be used to estimate community member preferences for chronic disease screening, but the mixed methods paper focused only on liver disease screening. Since there are many chronic disease conditions, and preferences for screening may vary depending on the condition, the authors may want to spend some time justifying their belief that these attributes and levels apply across conditions. While perhaps not relevant to the current manuscript, which focuses on the statistical design rather than the design of attributes and levels, a limitation of this DCE is that two of the attributes contain more than one construct. Screening conduct combines person and place, and while Source of information seems to focus on trust, the examples provided also include familiarity, the amount of time it takes for the information to be conveyed, the amount of detail conveyed, and the mode of communication, though not all of the levels' examples contain all of these constructs. Moreover, for both attributes, the sets of levels do not contain all combinations of construct elements (e.g. Nurse at your usual GP clinic is not included). Certainly all combinations of elements might not make sense, but this should be stated more explicitly. By including multiple constructs in one attribute, the authors are prevented from understanding the true elements within each level that influenced respondents' choices. Relatedly, I think the authors should justify why they chose to focus on negative predictive value in their "Quality and accuracy of the test results" attribute. Was there something in the focus groups or literature review that indicated negative predictive value was more important? Isn't positive predictive value equally important to community members? For instance, if positive predictive value is lower, then people will be diagnosed with a chronic condition though they don't truly have that condition. They may undergo unnecessary additional tests or initiate unnecessary treatment. They may experience unnecessary emotional distress. I also wonder what part of this attribute pertains to "Quality." For clarity, I recommend including footnotes in Table 2 and Table 4 to indicate which levels are continuous. The text notes that Quality and accuracy, Cost, and Wait time are all continuous, but currently only the Quality and accuracy attribute has a note about scale in Table 2, and those levels are listed as discrete as if it were a categorical attribute in Table 2 and Table 4. To avoid confusion, I would recommend reporting on these variables consistently. If the authors wish to provide estimates of the coefficients for results that are 85% and 95% accurate they can report those in-line, while reporting the per percentage point coefficient as for the other two continuous attributes. Minor concerns The citations for the Background paragraph and Methods paragraph on sample size calculations are from 2013 and 2014, respectively. If sample size calculation methods are truly evolving, one would expect more citations, and for most of them to be more recent than then. The first paragraph of the Methods does not include Step 1 in the list of steps, though it is included in Figure 1 and the next paragraph heading is "Step 1: Development of attributes and levels." The term S-estimate is described in the Methods but the S-estimate result from Table 2 is not interpreted in the Results. Does the S-estimate for the final design in Table 2 mean only 7.434 people need to take the DCE? Why do the reference levels change in Table 2 from the pilot survey to the final survey? Table 3 is inaccurately referenced in the Results, Step 5 paragraph. I believe the reference should be to Supplemental Table 1. Table 4 reports the coefficients from the pilot study. Apart from the confidence intervals and the constant, these are already reported in Table 2, as the "DCE design used for the final survey" column. Consider how to present this information in a way that avoids redundancy. The authors state that they used The DIRECT Checklist but they did not include the completed checklist in supplemental materials. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Infectious disease epidemiology and discrete choice experiments. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Zimba R. Peer Review Report For: A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey [version 1; peer review: 1 approved with reservations] . F1000Research 2025, 14 :96 ( https://doi.org/10.5256/f1000research.172410.r365931) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-96/v1#referee-response-365931 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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