Unified and simple sample size calculations for individual or cluster randomized trials with skewed or ordinal outcomes
本文提出一种基于有序累积概率模型的统一样本量计算方法，适用于个体或整群随机试验中偏态连续或有序结局，无需数据转换，通过模拟和实例验证其稳健性。
Abstract Sample size calculations can be challenging with skewed continuous outcomes in randomized controlled trials (RCTs). Standard t-test-based calculations may require data transformation, which may be difficult before data collection. Calculations based on individual and clustered Wilcoxon rank-sum tests have been proposed as alternatives, but these calculations for clustered data assume no ties in continuous outcomes, and clustered Wilcoxon rank-sum tests perform poorly with heterogeneous cluster sizes. Recent work has shown that continuous outcomes can be robustly analyzed using ordinal cumulative probability models. Analogously, sample size calculations for ordinal outcomes can be a robust design strategy for continuous outcomes. We show that Whitehead’s sample size calculations for independent ordinal outcomes can naturally extend to continuous outcomes. We extend these calculations to cluster RCTs using a design effect incorporating rank intraclass correlation coefficients. Therefore, we provide a unifying, simple approach for designing individual and cluster RCTs for continuous or ordinal outcomes that makes minimal assumptions on the distribution of the still-to-be-collected outcome. We conduct simulations to evaluate our approach’s performance and illustrate its application in multiple RCTs: an individual RCT with skewed continuous outcomes, a cluster RCT with skewed continuous outcomes, and a non-inferiority cluster RCT with an irregularly distributed count outcome.