Text Message intervention to improve function in people with low back pain (TEXT4myBACK): Statistical Analysis Plan

Text Message intervention to improve function in people with low back pain (TEXT4myBACK): Statistical Analysis Plan | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Text Message intervention to improve function in people with low back pain (TEXT4myBACK): Statistical Analysis Plan View ORCID Profile Sana Shan , View ORCID Profile Laurent Billot , View ORCID Profile Manuela Ferreira doi: https://doi.org/10.1101/2025.06.03.25328920 Sana Shan 1 The George Institute for Global Health , UNSW Sydney Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sana Shan For correspondence: sshan{at}georgeinstitute.org.au Laurent Billot 1 The George Institute for Global Health , UNSW Sydney Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Laurent Billot Manuela Ferreira 1 The George Institute for Global Health , UNSW Sydney Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Manuela Ferreira Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract The TEXT4myBACK trial aims to examine the effect of lifestyle-focused program delivered by text message on function in people with non-persistent low back pain. The lifestyle-focused intervention is delivered as a mobile phone text message over a period of 12 weeks. The statistical analysis plan pre-specifies the method of analysis for every outcome and key variables collected in the trial. The primary outcome is a composite endpoint incorporating measures of pain, functional status, patient reported impression of change, generic quality of life, levels of activity, hospital as well as medication use at 3-months follow-up that will be categorised into a hierarchy with number one ranked as most important according to clinical importance. The primary composite endpoint will be analysed using a hierarchical win ratio approach. Win-ratio approach maintains sequential assessment of outcomes while simultaneously assessing the effect of intervention for multiple outcomes as a single measure. Secondary, exploratory and subgroup group analysis have been pre-specified as well. 1 Administrative information 1.1 Study identifiers Australian New Zealand Clinical Trials Registry: ACTRN12618001263280, Date: 26 July 2018 NSLHD reference: RESP/19/005, ETH 13895 1.2 Revision history View this table: View inline View popup Download powerpoint 1.3 Contributors to the statistical analysis plan SS – Main author; developed the initial draft and prepared subsequent versions. LB, MF - Reviewed every draft and revision. 2 Introduction 2.1 Study synopsis The TEXT4myBACK trial 1 is a two-arm, parallel, double blinded (assessor and statistician blinded) randomised controlled trial designed to evaluate the effectiveness of a self-management text message intervention compared to minimal intervention in improving function in adults living in the community with non-persistent low back pain (LBP). The lifestyle-focused intervention is delivered as a mobile phone text message over a period of 12 weeks. It includes content incorporating education and advice on healthy lifestyle behaviours, including physical activity, mood and sleep, along with medication and use of care. Evidence-based and disease-specific education will also be provided, including information on the benign nature of the condition, the limited role of imaging, complex interventions (i.e. surgery) and strong analgesics (e.g. opioids). The development of the intervention in collaboration with the TEXT ME 2 research team, involvement of expert and consumer reviews during development phase as well as conducting a pilot study to test the feasibility of the identification and recruitment methods for both pharmacists and participants, have been discussed in detail in the protocol 1 . 2.2 Study population People living in Australia, who will contact the research team with non-persistent LBP (i.e. less than 12 weeks duration) and meet the study’s inclusion criteria will be invited to participate. A total of 304 participants will be randomised from the community, with recruitment commencing in the Sydney Metropolitan area and expanding to NSW and other states in Australia. 2.2.1 Inclusion Criteria To be included, individuals must meet all the following inclusion criteria: Be aged 18 years or older. Have an episode of non-specific LBP of less than 12 weeks duration, with or without the presence of leg pain. Have pain classified at least as ‘moderate’ or above in the pain scale of the SF-12: - During the past week, how much did pain interfere with your normal work (including work outside the home and housework)? 1-Not at all, 2-A little bit, 3-Moderately, 4-Quite a bit, 5-Extremely Familiarity with the use and access to, a phone that can receive text messages. 2.2.1 Exclusion Criteria Patients are not eligible if they meet any of the following criteria: Presence of serious pathology (described in detail in protocol 1 ). Current pregnancy. Spinal surgery within the preceding 12 months. Co-morbid health conditions that would prevent active participation in the physical activity programs (e.g. unstable angina, uncontrolled hypertension). Inadequate English to understand the text messages or complete outcome measures. Any disorder/reason that may reduce capacity to understand and give informed consent. 3 Study interventions All participants including those in the intervention and control groups will receive a welcome text informing that all study text messages will be signed as ‘#usyd’ and acknowledging their participation. All participants will receive text messages containing links to the online outcome measure surveys, utilisation of health care surveys and adverse events surveys at the specified time points. 1 These messages will advise participants to contact the research team via the study email or phone number if they feel unsafe or uncertain about their LBP. At 12 weeks, all participants will receive a text message informing them that they will no longer receive text messages with information about low back pain, but they will still receive links to complete the study surveys. In addition to the above-mentioned text messages, participants randomised to the control (minimal intervention) group will receive a text message (and an email if the participant does not use a smartphone) containing a link to access a LBP and healthy diet information package in REDCap. 3.1 Randomisation Randomisation will be provided off-site using the REDCap software to the intervention or control group in a 1:1 ratio. The participants will be randomised after they signed the consent form and completed the baseline survey. The TEXT ME team health counsellor will perform the randomisation once per week and will be the only person other than the trial coordinator who will be aware of the group allocation of the participant. The trial coordinator will also be responsible for uploading the participant’s details into the text message software. 3.2 Study treatment All consenting participants randomised to the intervention group will receive 4 text messages every week for the 12-week duration of the intervention period. These text messages will be semi-personalised providing advice, motivation and information to improve physical activity, sleep, mood, use of care and medication. Personalisation features include using participant’s preferred contact name, as well as sub-classification (e.g. messages from sleep domain will only be sent to those who reported sleep issue). Text message delivery will be managed by the TEXT ME software program and is designed to be delivered one-way (to the participant only). The software program will record all text message replies from participants for safety and potential descriptive analysis of their content. If the participant wishes to stop receiving text messages, they will be informed that they can reply “STOP’ at any time. Furthermore, participants in the intervention group will receive reminders (via text messages) every 4 weeks to complete the use of care surveys online. Detailed description is available in the protocol 1 on the intervention including key domains, days and different time slots as well as delivery time of specific domain messages. 4 Outcomes 4.1 Primary outcome The primary outcome is a composite outcome of the following variables at 3-months follow-up that will be categorised into a hierarchy (as shown below) with number one ranked as most important according to clinical importance: Overall self-reported health score (visual analogue scale VAS) from EQ5D-5L questionnaire Overall mobility measured on Likert scale from EQ-5D-5L questionnaire Average pain intensity in the past week Ability to perform daily activities from physical function scale 0-30 Amount of physical activity engaged within the past week (total time of moderate-vigorous physical activity in the previous week from Active Australia questionnaire) Number of visits made to healthcare professionals in the past 12 weeks for low back pain Any side or unpleasant effects experienced at 3 months (number of reported AEs) Number of hours spent sitting or being inactive in a normal week (Average sum of time spent in 9 sedentary activities in a week in Sedentary Behaviour Questionnaire ((Typical weekday hours x 5) + (Typical weekend hours x 2) / 7) The perceived overall change in symptoms from Global impression of change questionnaire Amount of pain medicines taken in the past 12 weeks to manage low back pain Anxiety and depression linked to low back pain measured on Likert scale from EQ-5D-5L 4.2 Secondary outcomes The secondary outcomes will include following outcomes at 3, 6 and 12 months: Health state (utility score) calculated from EQ5D-5L using value sets for Australian population. Average pain intensity in the past week Ability to perform daily activities from physical function scale Amount of physical activity engaged with in the past week as a binary variable from Active Australia Insufficiently Active (1-149 min/week) and Sufficiently Active (≥150 min/week)) The perceived overall change in symptoms from Global impression of change (0 as improvement) Hours spent per week in sedentary behaviour from Sedentary behaviour questionnaire eHealth literacy questionnaire 4.3 Safety outcomes Smallest worthwhile effect stated as a baseline survey to analyse whether the score they achieve at 3 months is meaningful to the individual Health care utilisation survey asking about any referrals to health professionals or other services and medications prescribed or purchased related to their LBP every 4 weeks 4.4 Safety outcomes Occurrence of any adverse event (AE) or serious adverse event (SAE) can be reported in the study outcome surveys completed at 3, 6 and 12 months. 5 Analysis principles 5.1 Sample size A sample size of 152 per group (total of 304 participants) will provide 90% power to detect an effect size of 0.4, assuming a standard deviation (SD) of 7 and allowing for a loss to follow-up rate of 15% at 3 months based on the between-group difference on our primary outcome patient specific functional scale (0-30 points) at 3 months (primary endpoint). 5.2 Software Analyses will be conducted primarily using SAS Enterprise Guide (version 8.3 or above) and R (version 4.0.0 or above). 5.3 Interim analyses No formal interim analyses were conducted during the study. 5.4 Multiplicity adjustment Statistical tests are to be two-sided with a nominal level of α set at 5%. Analyses of the primary outcome (win ratio composite) will be unadjusted for multiplicity. Given that secondary outcomes are mostly components of the primary outcome, no adjustment for multiplicity will be applied. Instead, the analysis of secondary outcomes will be considered exploratory and used to describe the contribution of each component to the overall effect. Secondary outcomes will be analysed regardless of significance for the primary outcome. 5.5 Datasets analysed 5.5.1 Analysis populations The analysis population is intention-to-treat (ITT) population. The intention-to-treat (ITT) analysis set will be used to assess both effectiveness and safety. 5.5.1 Analysis strategy For all outcomes, the analyses will be performed on the ITT population using all available data. It is expected that the rate of missingness will be around 15%, multiple imputation techniques will be considered if missingness is > 15% as part of the sensitivity analysis to impute missing data. Participants who withdraw or are lost to follow-up will not be replaced but their available data will be included in an intention to treat analysis unless specified prior to analysis. By withdrawing from the study, no further data will be collected, and no further contact will be made with the participants. 6 Planned Analyses 6.1 Subject disposition The flow of patients through the trial will be displayed in a CONSORT 3 (Consolidated Standards of Reporting Trials) diagram [see Figure 1 ]. The report will include the following: the number of participants assessed for eligibility, reasons for exclusion, the number of enrolled participants, the number available at 3, 6 and 12-months follow-up timepoints within the intervention and control arm. The flow chart will also include the number of participants lost to follow-up/ withdrew from the study as well as the reason for withdrawal at each timepoint. 6.2 Baseline comparison 6.2.1 Patient characteristics Description of baseline patient characteristics will be presented by treatment group. Discrete variables will be summarised by frequencies and percentages. Percentages will be calculated according to the number of patients in whom data are available. Continuous variables will be summarised by using mean and SD, and median and interquartile range (Q1-Q3). Baseline measures for all patients will be tabulated for the variables listed below Anthropometrics: age (years), sex, weight (kg), height (cm) and BMI (kg/m 2 ) Demographics and socio-economic status including education, marital status, employment status Sleep issues, medication use LBP duration and symptom distribution Self-Administered Comorbidity Questionnaire – This is a validated instrument for the assessment of comorbidities 4 , which has been previously used to record comorbidities of patients with LBP 5 , 6 6.3 Follow-up assessments All assessments performed and interventions received during follow-up will be described by treatment group. Details are presented in mock tables. No formal statistical tests are planned for these variables. 6.4 Analysis of the primary outcome The primary composite endpoint will be analysed using a hierarchical win ratio approach. Win-ratio approach maintains sequential assessment of outcomes while simultaneously assessing the effect of intervention for multiple outcomes as a single measure. 6.4.1 Main analysis The outcomes will be ranked to reflect their clinical importance. A threshold will be defined indicating a clinically meaningful difference for each outcome. The decision process is explained in the table below. All participants in the intervention group will be paired with every participant in the control group (152 x 152 = 23,104 pairs). Within each pair, the response to each outcome will be compared in a hierarchical fashion to determine whether it is a win, a loss or a tie. If there’s no difference in response to a particular outcome within that pair, the decision is based on the response to the next ranked outcome. Across all pairs, the ratio of wins and losses will be obtained. The results will be presented as win ratio ( R W ) with 95% confidence intervals (CI) obtained using Finkelstein and Schoenfeld 7 method as explained in detail by Boentert et al 8 . “The total number of participants is represented by N= N + N C . For all possible pairs, the winner is assigned a score of 1, −1 or 0 based on a win, a loss or a tie respectively. The scores from all pairs are summed up for each participant with a positive integer U i indicating more wins than losses and vice versa. T statistic is calculated using a formula: where Di = 1 if participant i is on the treatment and Di= 0 if participant i is on the control. Under the null hypothesis of no true difference between treatment and control, T has variance V where: If T follows a normal distribution, z-score can be derived as z = T /√ V , and obtain the p-value for a two-sided test for T given by 2xP(Z > z), where Z follows a standard normal distribution. An approximate 95% CI for R W can also be calculated based on the z-score by first deriving the standard error (SE) for log R W : The 95% CI around . The limits are then exponentiated to derive CI around R W .” View this table: View inline View popup 6.5 Analysis of the secondary outcomes For continuous outcomes including patient-specific functional scale, average pain intensity during the past week, Health utility score from EQ5D and hours per week spent in sedentary behaviour, linear mixed models will be used to assess the effect of the treatment group. The model will include outcome data collected at every post-baseline visit, i.e. at months 3, 6 and 12. Fixed effects will include the randomised treatment allocation, the follow-up time points as a categorical variable with 3 levels, the interaction between treatment and time-point as well as the baseline measurement of the outcome. Within-patient correlations will be modelled using a repeated patient effect. The overall effect of the intervention will be estimated as the mean difference and 95% CI between the intervention and control arm over the entire follow-up i.e. by combining data obtained across all visits. The effect of the intervention at specific time points (e.g. at Month 12) will be estimated from the same model using contrasts. The outcomes of participant’s global impression of change (0 as improvement) will be analysed on an ordinal scale. The distribution of ordinal outcome variable between randomised groups will be analysed at 3, 6 and 12 months using a mixed model with repeated measures (as mentioned above) with multinomial distribution and a cumulative logit link function. The overall effect of the intervention will be estimated as odds ratio with 95% CI. Similar approach will be used for the analysis of binary variable of physical activity engagement (sufficiently active vs. insufficiently active) with binomial distribution and logit link function. The distribution of eHealth literacy outcome related to knowledge and confidence in finding and understanding digital health information will be analysed descriptively for each follow-up timepoint for 7 independent scales from eHLQ questionnaire with averaged score ranging from 1 – 4. Each scale consists of 4 – 6 items with assigned value 1 strongly disagree – 4 strongly agree. The results will be presented as mean and standard deviation. 6.6 Sensitivity analyses No Sensitivity analyses are planned. 6.7 Treatment of missing data To evaluate the impact of missing data, multiple imputation 9 will employed generating five datasets to estimate changes from baseline at 3 months for all randomised participants. The imputation model will include age at baseline, sex, treatment group, baseline values of outcome measures (listed in section 5.1 ) as well their follow-up values at 3, 6 and 12 months. Win ratios will be calculated separately for each imputed dataset using the same approach a mentioned in section 7.4.1, the results will be pooled to produce a single overall estimate. 6.8 Per-protocol analysis No per-protocol analysis will be carried out. 6.9 Analysis of the exploratory outcomes Analysis of exploratory outcomes including smallest worthwhile effect (SWE) (binary outcome of whether smallest worthwhile effect achieved or not) at month 3 and health utilisation over 12 months will be analysed using descriptive statistics. Using a heatmap, the trends in healthcare utilisation (counts of visits to healthcare professionals) will be visualised for each participant over time (at every 4 weeks from baseline to 12 months). Analysis of MBS and PBS data will be analysed at a later stage and will not be included as part of this analysis. 6.10 Analysis of safety outcomes Safety outcomes of all-cause and cause-specific SAEs will be summarised as the number and proportion of patients experiencing at least one event. Adverse events will be summarised by category of event and overall numbers of events. In addition to the number of patients with at least one event, we will report the total number of events. A listing of all AEs and SAEs will be reported (in an appendix). 6.11 Subgroup analyses Pre-specified subgroup analyses will be carried out, irrespective of whether there is a significant treatment effect on the primary outcome. These subgroup analyses will only be performed in the ITT population. Subgroups based on pre-randomisation patient characteristics assessed before randomisation are defined as follows: Age category (<65 vs. ≥65 years) Sex Baseline physical function score (=12) Analysis for each subgroup will be performed by repeating the main analysis (as described in section 7.4.1) separately within each subgroup. The results will be displayed on a forest plot. No p-value for heterogeneity will be computed given that the analysis is only performed separately within each subgroup. Data Availability The manuscript is a statistical analysis plan and does not contain any results. 8 Appendix 1: Proposed Tables and figures Download figure Open in new tab Figure 1: Consort flowchart View this table: View inline View popup Table 1. Baseline characteristics View this table: View inline View popup Table 2. Back pain assessment at baseline View this table: View inline View popup Table 3. Baseline assessment of all outcomes View this table: View inline View popup Download powerpoint Table 4. Descriptive statistics for eHealth Literacy Questionnaire at all time-points View this table: View inline View popup Table 5. Win ratio analysis of the primary outcome and its components View this table: View inline View popup Download powerpoint Table 6. Analysis of secondary outcomes – model results View this table: View inline View popup Download powerpoint Table 7. Exploratory outcomes View this table: View inline View popup Table 8. Summary of adverse events at all follow-up timepoints Listing 1. AE listing Figure 2: Forest plot for subgroup analysis This will be done only for the overall Win ratio analysis. Within each subgroup, it will show the Win Ratio and 95% CI. No p-value will be displayed. Download figure Open in new tab Figure 3: Flow diagram of win ratio decisions Download figure Open in new tab Figure 4: Bar chart of EQ5D-5L across all timepoints (Baseline to 12 months) Figure 5: Mean plot of health utility score (EQ5D) over time (Baseline to 12 Months) by treatment groups Programming notes: Display raw means on the graph as numbers near each dot and denominators below the x-axis Figure 6: Heatmap of healthcare utilization over time 7 References 1. ↵ Ferreira M. FP , Prior J. , Fritsch C. , McLachlan A. , Chow C. , Redfern J. Protocol ‘Text Message intervention to improve function in people with low back pain’,version 7, NSLHD reference: RESP/19/005, ETH 13895 . 2022 . 2. ↵ Chow CK , Redfern J , Hillis GS , Thakkar J , Santo K , Hackett ML , et al. Effect of lifestyle-focused text messaging on risk factor modification in patients with coronary heart disease: a randomized clinical trial . Jama . 2015 ; 314 ( 12 ): 1255 – 63 . OpenUrl CrossRef PubMed 3. ↵ Kf S. CONSORT 2010 statement: updated guidelines for reporting parallel group randomized trials . Ann Intern Med . 2010 ; 152 : 1 – 7 . OpenUrl CrossRef PubMed Web of Science 4. ↵ Sangha O , Stucki G , Liang MH , Fossel AH , Katz JN. The Self-Administered Comorbidity Questionnaire: a new method to assess comorbidity for clinical and health services research . Arthritis Care & Research: Official Journal of the American College of Rheumatology . 2003 ; 49 ( 2 ): 156 – 63 . OpenUrl 5. ↵ Bethge M , Mattukat K , Fauser D , Mau W. Rehabilitation access and effectiveness for persons with back pain: the protocol of a cohort study (REHAB-BP, DRKS00011554) . BMC Public Health . 2018 ; 18 : 1 – 11 . OpenUrl CrossRef PubMed 6. ↵ Schmidt CT , Ward RE , Suri P , Kiely DK , Goldstein R , Pensheng N , et al. Which neuromuscular attributes are associated with changes in mobility among community-dwelling older adults with symptomatic lumbar spinal stenosis? Archives of physical medicine and rehabilitation . 2018 ; 99 ( 11 ): 2190 – 7 . OpenUrl PubMed 7. ↵ Finkelstein DM , Schoenfeld DA. Combining mortality and longitudinal measures in clinical trials . Statistics in medicine . 1999 ; 18 ( 11 ): 1341 – 54 . OpenUrl CrossRef PubMed Web of Science 8. ↵ Boentert M BK , Díaz-Manera J , Dimachkie MM , Hamed A , Riou França L , Thibault N , Shukla P , Ishak J , Caro JJ. Applying the win ratio method in clinical trials of orphan drugs: an analysis of data from the COMET trial of avalglucosidase alfa in patients with late-onset Pompe disease . Orphanet Journal of Rare Diseases . 2024 . 9. ↵ White IR , Royston P , Wood AM. Multiple imputation using chained equations: issues and guidance for practice . Statistics in medicine . 2011 ; 30 ( 4 ): 377 – 99 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted June 04, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. 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Share Text Message intervention to improve function in people with low back pain (TEXT4myBACK): Statistical Analysis Plan Sana Shan , Laurent Billot , Manuela Ferreira medRxiv 2025.06.03.25328920; doi: https://doi.org/10.1101/2025.06.03.25328920 Share This Article: Copy Citation Tools Text Message intervention to improve function in people with low back pain (TEXT4myBACK): Statistical Analysis Plan Sana Shan , Laurent Billot , Manuela Ferreira medRxiv 2025.06.03.25328920; doi: https://doi.org/10.1101/2025.06.03.25328920 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Rehabilitation Medicine and Physical Therapy Subject Areas All Articles Addiction Medicine (569) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4442) Dentistry and Oral Medicine (444) Dermatology (383) Emergency Medicine (609) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1511) Epidemiology (15231) Forensic Medicine (30) Gastroenterology (1126) Genetic and Genomic Medicine (6610) Geriatric Medicine (668) Health Economics (998) Health Informatics (4542) Health Policy (1370) Health Systems and Quality Improvement (1613) Hematology (543) HIV/AIDS (1266) Infectious Diseases (except HIV/AIDS) (15923) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (147) Nephrology (668) Neurology (6607) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1146) Occupational and Environmental Health (957) Oncology (3338) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1693) Pharmacology and Therapeutics (692) Primary Care Research (712) Psychiatry and Clinical Psychology (5448) Public and Global Health (9239) Radiology and Imaging (2203) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (596) Sexual and Reproductive Health (714) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a02005a9ff081640',t:'MTc3OTgzMjUwNQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();