Hallgren Kevin A, Atkins David C, Witkiewitz Katie
Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, Washington.
Department of Psychology, Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, Albuquerque, New Mexico.
J Stud Alcohol Drugs. 2016 Nov;77(6):986-991. doi: 10.15288/jsad.2016.77.986.
Statistical analyses in alcohol clinical trials often use longitudinal daily drinking data (e.g., percentage of drinking days) to test treatment efficacy. Such data can be aggregated and analyzed in many ways. To assess how statistical analytic decisions may influence substantive results, the current report compares different aggregation methods (e.g., computing percentages of drinking days vs. using daily binary indicators of drinking) and statistical methods (i.e., least squares regression, linear mixed-effects models [LMM], generalized linear mixed models [GLMM], and generalized estimating equations [GEE]) for testing the effects of treatment on drinking outcomes in clinical trials.
A simulation study repeatedly resampled daily drinking data from the treatment period of the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) Study at different sample sizes. Treatment effects in each data set were modeled using different aggregation and statistical methods.
Type I error rates were near the expected rate for most models but on occasion were mildly elevated when disaggregated daily drinking data were analyzed using GLMM or GEE with an exchangeable correlation structure. Most methods yielded similar statistical power, although power decreased when modeling disaggregated daily drinking with GLMM and had mixed increases and decreases when the longitudinal nature of data was ignored by using fully aggregated data with independent samples t tests.
When testing treatment main effects, relatively simpler statistical methods with fewer repeated measures may perform equally well or better than more complicated methods. Patterns of significance and treatment effect size estimates are likely comparable across most studies that use different aggregation and statistical methods, but differences between these methods may occasionally have an important impact on conclusions in clinical trials.
酒精临床试验中的统计分析通常使用纵向每日饮酒数据（如饮酒天数的百分比）来检验治疗效果。此类数据可以通过多种方式进行汇总和分析。为了评估统计分析决策如何影响实质性结果，本报告比较了不同的汇总方法（如计算饮酒天数的百分比与使用饮酒的每日二元指标）和统计方法（即最小二乘法回归、线性混合效应模型[LMM]、广义线性混合模型[GLMM]和广义估计方程[GEE]），以检验临床试验中治疗对饮酒结果的影响。
一项模拟研究对酒精依赖联合药物治疗与行为干预（COMBINE）研究治疗期的每日饮酒数据按不同样本量进行重复重采样。使用不同的汇总和统计方法对每个数据集中的治疗效果进行建模。
大多数模型的I型错误率接近预期水平，但在使用具有可交换相关结构的GLMM或GEE分析每日饮酒数据时，偶尔会略有升高。大多数方法产生的统计功效相似，尽管使用GLMM对每日饮酒数据进行分解建模时功效会降低，而使用完全汇总数据和独立样本t检验忽略数据的纵向性质时，功效则有增有减。
在检验治疗主效应时，具有较少重复测量的相对简单的统计方法可能与更复杂的方法表现相当或更好。在大多数使用不同汇总和统计方法的研究中，显著性模式和治疗效果大小估计可能具有可比性，但这些方法之间的差异偶尔可能会对临床试验的结论产生重要影响。
