Methods for Analysis of Pre-Post Data in Clinical Research: A Comparison of Five Common Methods.
Methods for Analysis of Pre-Post Data in Clinical Research: A Comparison of Five Common Methods.
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DOI：
10.4172/2155-6180.1000334
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发表时间：
2017-02-24
期刊：
影响因子：
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通讯作者：
Gebregziabher, Mulugeta
中科院分区：
文献类型：
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作者：
O'Connell, Nathaniel S;Dai, Lin;Gebregziabher, Mulugeta
通常，重复测量的数据被总结为治疗前和治疗后的测量结果。文献中存在用于估计和检验治疗效应的各种方法，包括ANOVA、协方差分析（ANCOVA）和线性混合模型（LMM）。在前两种方法下，结果可以建模为治疗后测量（ANOVA-POST或ANCOVA-POST）或前后测量之间的变化评分（ANOVA-CHANGE，ANCOVA-CHANGE）。在LMM中，结果被建模为具有或不具有Kenward-Rogers调整的响应向量。我们认为在文献中常见的五种方法，并讨论他们在支持模拟和方差的理论推导。与现有文献一致，我们的结果表明，每种方法都能得到无偏的治疗效果估计值，并且基于估计值的精度、95%的覆盖概率和把握度，将变化评分或治疗后评分作为结果的ANCOVA建模被证明是最有效的。我们进一步证明了每一种方法的真实的数据的例子，在真实的临床背景下进行比较。
Often repeated measures data are summarized into pre-post-treatment measurements. Various methods exist in the literature for estimating and testing treatment effect, including ANOVA, analysis of covariance (ANCOVA), and linear mixed modeling (LMM). Under the first two methods, outcomes can either be modeled as the post treatment measurement (ANOVA-POST or ANCOVA-POST), or a change score between pre and post measurements (ANOVA-CHANGE, ANCOVA-CHANGE). In LMM, the outcome is modeled as a vector of responses with or without Kenward-Rogers adjustment. We consider five methods common in the literature, and discuss them in terms of supporting simulations and theoretical derivations of variance. Consistent with existing literature, our results demonstrate that each method leads to unbiased treatment effect estimates, and based on precision of estimates, 95% coverage probability, and power, ANCOVA modeling of either change scores or post-treatment score as the outcome, prove to be the most effective. We further demonstrate each method in terms of a real data example to exemplify comparisons in real clinical context.