Accounting for dropout bias using mixed-effects models.
Accounting for dropout bias using mixed-effects models.
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DOI：
10.1081/bip-100104194
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发表时间：
2001-02-01
影响因子：
1.1
通讯作者：
David, S R
中科院分区：
文献类型：
--
作者：
Mallinckrodt, C H;Clark, W S;David, S R
通常通过比较结果指标随时间的变化来评估治疗效果。然而，当受试者在完成研究之前中断（退出）时，纵向数据的有效分析可能会出现问题。本研究评估了基于可能性的重复测量分析 (MMRM) 与固定效应方差分析的优点，其中使用最后观察结转方法 (LOCF) 来估算缺失值，以解决脱落偏差。对模拟数据和随机临床试验的数据进行了比较。在模拟数据中引入受试者脱落以产生可忽略和不可忽略的缺失。在每个模拟场景中，平均而言，MMRM 对治疗组从基线到终点的平均变化差异的估计明显比 LOCF 的估计更接近真实值。 MMRM 的标准误差和置信区间准确地反映了估计的不确定性，而 LOCF 的标准误差和置信区间则低估了不确定性。
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.