Background: Many methods have been suggested for analyzing the modified Rankin Scale (mRS). However, there lacks a unified approach to analysis and sample size determination that properly uses the ordinal nature of the data. We propose a simple method for CI estimation and corresponding sample size determination. Methods: We quantify treatment effect by the win probability (WinP) that a randomly selected patient in the treatment group has an equal or a better mRS score than a patient in the control group. Thus, a win probability of 0.5 means no effect, likened to a draw in competitive sports. We estimate the win probability and its SE based on the ranks of mRS scores, where tied scores are handled by average ranks. Corresponding methods for hypothesis testing, CI estimation, and sample size determination are derived. The methods are evaluated with a simulation study based on real data from 10 randomized stroke trials that used mRS as the outcome measure. Results: Simulation results demonstrated that the methods performed very well in terms of CI coverage, tail errors, and assurance to achieving the prespecified precision. Because the methods are very simple, we implemented them in an Excel spreadsheet, requiring only user inputs on frequencies of mRS scores in 2 comparison groups. Conclusions: Sound statistical methods are important for the success of randomized stroke trials. The proposed methods and associated spreadsheet should prove useful for stroke researchers in the planning and analysis of randomized trials. Meta-analysis has also been made easy for trials with ordinal scores.

Background and Aims Most pharmaceutical clinical trials for inflammatory bowel disease (IBD) are placebo-controlled and require effect size estimation for a drug relative to placebo. We compared expected effect sizes in sample size calculations (SSCs) to actual effect sizes in IBD clinical trials. Methods MEDLINE, EMBASE, CENTRAL, and the Cochrane library were searched from inception to March 26, 2021, to identify placebo-controlled induction studies for luminal Crohn’s disease (CD) and ulcerative colitis (UC) that reported an SSC and a primary endpoint of clinical remission/response. Expected effects were subtracted from actual effects, and interquartile ranges (IQRs) for each corresponding median difference were calculated. Linear regression was used to assess whether placebo or drug event rate misspecifications were responsible for these differences. Results Of eligible studies, 36.9% (55/149) were excluded because of incomplete SSC reporting, yielding 94 studies (46 CD, 48 UC). Treatment effects were overestimated in CD for remission (–12.6% [IQR: –16.3% to –1.6%]), in UC for remission (–10.2% [IQR: –16.5% to –5.6%]), and in CD for response (–15.3% [IQR: –27.1% to –5.8%]). Differences observed were due to overestimated drug event rates, whereas expected and actual placebo event rates were similar. A meta-regression demonstrated associations between overestimated treatment effect sizes and several trial characteristics: isolated ileal disease, longer CD duration, extensive colitis (UC), single-centre, phase 2, no endoscopic endpoint component (UC). Conclusion Overestimation of IBD therapy efficacy rates resulted in smaller-than-expected treatment effects. These results should be used to inform SSCs and trial design for IBD drug development.

Data on the Likert scale are ubiquitous in medical research, including randomized trials. Statistical analysis of such data may be conducted using the means of raw scores or the rank information of the scores. In the context of parallel-group randomized trials, we quantify treatment effects by the probability that a subject in the treatment group has a better score than (or a win over) a subject in the control group. Asymptotic parametric and nonparametric confidence intervals for this win probability and associated sample size formulas are derived for studies with only follow-up scores, and those with both baseline and follow-up measurements. We assessed the performance of both the parametric and nonparametric approaches using simulation studies based on real studies with Likert item and Likert scale data. The simulation results demonstrate that even without baseline adjustment, the parametric methods did not perform well, in terms of bias, interval coverage percentage, balance of tail error, and assurance of achieving a pre-specified precision. In contrast, the nonparametric approach performed very well for both the unadjusted and adjusted win probability. We illustrate the methods with two examples: one using Likert item data and the other using Like scale data. We conclude that non-parametric methods are preferable for two-group randomization trials with Likert data. Illustrative SAS code for the nonparametric approach using existing procedures is provided.effect size, health related quality of life, Likert scale, Mann-Whitney statistic, modified Rankin scale, number needed to treat, ridit, standardized mean difference | INTRODUCTIONLikert data are ubiquitous in quantitative research involving outcome measurements of non-physical phenomena. The original Likert approach involved the presentation of a series of items that are used to measure an underlying trait.

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