回归均值调整的截断选择单臂前后测研究样本量确定

中文摘要 AI 辅助

基于极端基线值入组参与者的研究易受回归均值（RTM）影响，因此即使没有治疗，也会预期观察到某些前后变化。现有方法回顾性地估计和分解RTM。我们将该框架扩展到前瞻性研究设计，通过推导连续型、截断选择、单臂前后测研究的闭式样本量方法。该方法将预期总变化划分为RTM预期部分和残余治疗效果部分，并为检测后者提供研究效能。它使用一般双变量正态RTM表达式，允许基线和随访方差不同，并纳入前后测变化的相应条件方差。据我们所知，尚无已发表的方法或软件在此情境下实现基于截断的RTM框架用于前瞻性闭式样本量确定。Stata命令power onemean_rtm提供样本量和最小可检测效应、正态理论效能评估、失访调整和敏感性分析的解决方案。跨58种场景的蒙特卡洛模拟表明，I类错误控制准确，且随着样本量增加，实现效能迅速接近名义效能，闭式计算在非常小的样本量下通常保守。

英文摘要

Studies enrolling participants on the basis of an extreme baseline value are susceptible to regression to the mean (RTM), such that some observed pre-post change is expected even without treatment. Existing methods estimate and decompose RTM retrospectively. We extend this framework to prospective study design by deriving a closed-form sample-size method for continuous, cutoff-selected, single-arm pre-post studies. The method partitions anticipated total change into that expected from RTM and the residual treatment effect, and powers the study to detect the latter. It uses the general bivariate-normal RTM expression, allowing baseline and follow-up variances to differ, and incorporates the corresponding conditional variance of the pre-post change. To our knowledge, no published method or software implements this cutoff-based RTM framework for prospective closed-form sample-size determination in this setting. The Stata command power onemean_rtm provides solutions for sample size and minimum detectable effect, normal-theory power evaluation, attrition adjustment, and sensitivity analysis. Monte Carlo simulation across 58 scenarios demonstrated accurate Type I error control and showed that achieved power rapidly approached nominal power as sample size increased, with the closed-form calculation generally conservative at very small sample sizes.

发表机构

• University of California, San Francisco（旧金山加州大学）

机构由 AI 辅助整理，请以论文原文为准。