Abstract

When designing repeated measures studies, both the amount and the pattern of missing outcome data can affect power. The chance that an observation is missing may vary across measurements, and missingness may be correlated across measurements. For example, in a physiotherapy study of patients with Parkinson’s disease, increasing intermittent dropout over time yielded missing measurements of physical function. In this example, we assume data are missing completely at random, since the chance that a data point was missing appears to be unrelated to either outcomes or covariates. For data missing completely at random, we propose noncentral F power approximations for the Wald test for balanced linear mixed models with Gaussian responses. The power approximations are based on moments of missing data summary statistics. The moments were derived assuming a conditional linear missingness process. The approach provides approximate power for both complete-case analyses, which include independent sampling units where all measurements are present, and observed-case analyses, which include all independent sampling units with at least one measurement. Monte Carlo simulations demonstrate the accuracy of the method in small samples. We illustrate the utility of the method by computing power for proposed replications of the Parkinson’s study.

摘要

在设计重复测量研究时，缺失结果数据的数量和模式都会影响功效。观察缺失的机会可能因测量而异，并且缺失可能与测量相关。例如，在一项针对帕金森病患者的物理治疗研究中，随着时间的推移，越来越多的间歇性辍学导致身体功能测量缺失。在此示例中，我们假设数据完全随机缺失，因为数据点缺失的可能性似乎与结果或协变量无关。对于完全随机缺失的数据，我们提出非中心F具有高斯响应的平衡线性混合模型的 Wald 检验的功率近似值。功率近似基于缺失数据汇总统计的时刻。这些矩是在假设有条件的线性缺失过程的情况下得出的。该方法为完整个案分析和观察个案分析提供了近似功效，前者包括存在所有测量值的独立抽样单元，后者包括所有独立抽样单元和至少一个测量值。蒙特卡罗模拟证明了该方法在小样本中的准确性。我们通过对帕金森氏症研究的拟议复制的计算能力来说明该方法的效用。