Yoo Bongin
Global Biometric Sciences, Bristol-Myers Squibb Company, Wallingford, CT 06492, USA.
Pharm Stat. 2010 Oct-Dec;9(4):298-312. doi: 10.1002/pst.396.
In this paper, a simulation study is conducted to systematically investigate the impact of dichotomizing longitudinal continuous outcome variables under various types of missing data mechanisms. Generalized linear models (GLM) with standard generalized estimating equations (GEE) are widely used for longitudinal outcome analysis, but these semi-parametric approaches are only valid under missing data completely at random (MCAR). Alternatively, weighted GEE (WGEE) and multiple imputation GEE (MI-GEE) were developed to ensure validity under missing at random (MAR). Using a simulation study, the performance of standard GEE, WGEE and MI-GEE on incomplete longitudinal dichotomized outcome analysis is evaluated. For comparisons, likelihood-based linear mixed effects models (LMM) are used for incomplete longitudinal original continuous outcome analysis. Focusing on dichotomized outcome analysis, MI-GEE with original continuous missing data imputation procedure provides well controlled test sizes and more stable power estimates compared with any other GEE-based approaches. It is also shown that dichotomizing longitudinal continuous outcome will result in substantial loss of power compared with LMM.
本文进行了一项模拟研究，以系统地调查在各种类型的缺失数据机制下，对纵向连续结果变量进行二分法的影响。具有标准广义估计方程（GEE）的广义线性模型（GLM）被广泛用于纵向结果分析，但这些半参数方法仅在完全随机缺失数据（MCAR）的情况下有效。另外，开发了加权GEE（WGEE）和多重填补GEE（MI-GEE）以确保在随机缺失（MAR）情况下的有效性。通过模拟研究，评估了标准GEE、WGEE和MI-GEE在不完整纵向二分结果分析中的性能。为了进行比较，基于似然的线性混合效应模型（LMM）用于不完整纵向原始连续结果分析。专注于二分结果分析，与任何其他基于GEE的方法相比，采用原始连续缺失数据插补程序的MI-GEE提供了良好控制的检验规模和更稳定的功效估计。研究还表明，与LMM相比，对纵向连续结果进行二分法会导致功效大幅损失。
