Overall J E, Shobaki G, Shivakumar C, Steele J
Department of Psychiatry and Behavioral Science, University of Texas Medical School, Houston 77030, USA.
Psychopharmacol Bull. 1998;34(1):25-33.
Statistical models for calculating sample sizes for controlled clinical trials often fail to take into account the negative impact that dropouts have on the power of intent-to-treat analyses. Empirically defined dropout correction coefficients are proposed to adjust sample sizes for endpoint analysis of variance (ANOVA) and analysis of covariance (ANCOVA) that have been initially calculated assuming complete data. The implications of type of analysis (change-score ANOVA or ANCOVA), correlational structure of the repeated measurements (compound symmetry or autoregressive), and percentage of dropouts (20% or 30%) are considered, together with other less influential design and data parameters. We recommend the use of ANCOVA to correct for baseline differences and for time-in-study if there is a nonspecific change across time. Given a realistic autoregressive (order 1) correlational structure for the repeated measurements and a proposed endpoint ANCOVA, the empirical results support the common practice of increasing calculated sample size by the anticipated number of dropouts. The previous rationale has been to retain a requisite number of "completers" on which to base statistical inferences. We believe the present results provide the first documentation of the relevance of that strategy for intent-to-treat analyses in which the incomplete data for dropouts must be included. Based on comparative power analyses, the strategy also seems appropriate for maintaining the power of mixed-model regression analyses, simple regression on a normalized time scale, and analyses of trends fitted to imputed scores for dropouts.
用于计算对照临床试验样本量的统计模型常常未能考虑到失访对意向性分析效能的负面影响。本文提出了根据经验定义的失访校正系数，用于调整最初在假设数据完整的情况下计算出的用于方差分析（ANOVA）和协方差分析（ANCOVA）终点分析的样本量。研究考虑了分析类型（变化分数ANOVA或ANCOVA）、重复测量的相关结构（复合对称性或自回归性）以及失访百分比（20%或30%）的影响，同时也考虑了其他影响较小的设计和数据参数。如果随时间存在非特异性变化，我们建议使用ANCOVA来校正基线差异和研究时间。对于重复测量具有实际的自回归（一阶）相关结构以及提议的终点ANCOVA，实证结果支持将计算出的样本量按预期失访数量增加这一常见做法。以往的理论依据是保留足够数量的“完成者”以进行统计推断。我们认为目前的结果首次证明了该策略对于意向性分析的相关性，在这种分析中必须纳入失访者的不完整数据。基于比较效能分析，该策略似乎也适用于维持混合模型回归分析、标准化时间尺度上的简单回归以及针对失访者插补分数拟合趋势分析的效能。
