Mallinckrodt C H, Clark W S, David S R
Eli Lilly & Co, Lilly Corporate Center, Indianapolis, Indiana 46285, USA.
J Biopharm Stat. 2001 Feb-May;11(1-2):9-21. doi: 10.1081/BIP-100104194.
Treatment effects are often evaluated by comparing change over time in outcome measures. However, valid analyses of longitudinal data can be problematic when subjects discontinue (dropout) prior to completing the study. This study assessed the merits of likelihood-based repeated measures analyses (MMRM) compared with fixed-effects analysis of variance where missing values were imputed using the last observation carried forward approach (LOCF) in accounting for dropout bias. Comparisons were made in simulated data and in data from a randomized clinical trial. Subject dropout was introduced in the simulated data to generate ignorable and nonignorable missingness. Estimates of treatment group differences in mean change from baseline to endpoint from MMRM were, on average, markedly closer to the true value than estimates from LOCF in every scenario simulated. Standard errors and confidence intervals from MMRM accurately reflected the uncertainty of the estimates, whereas standard errors and confidence intervals from LOCF underestimated uncertainty.
治疗效果通常通过比较结果指标随时间的变化来评估。然而，当受试者在完成研究之前退出（失访）时，对纵向数据进行有效的分析可能会出现问题。本研究评估了基于似然的重复测量分析（MMRM）与固定效应方差分析的优缺点，后者使用末次观察结转法（LOCF）对缺失值进行插补，以解决失访偏倚问题。在模拟数据和一项随机临床试验的数据中进行了比较。在模拟数据中引入了受试者失访情况，以产生可忽略和不可忽略的缺失值。在模拟的每种情况下，MMRM对治疗组从基线到终点的平均变化差异的估计，平均而言，比LOCF的估计明显更接近真实值。MMRM的标准误和置信区间准确反映了估计的不确定性，而LOCF的标准误和置信区间低估了不确定性。
