On the multiple imputation variance estimator for control-based and delta-adjusted pattern mixture models
On the multiple imputation variance estimator for control-based and delta-adjusted pattern mixture models
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DOI：
10.1111/biom.12702
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发表时间：
2017-12-01
期刊：
影响因子：
1.9
通讯作者：
Tang, Yongqiang
中科院分区：
文献类型：
--
作者：
Tang, Yongqiang
基于控制的模式混合模型（PMM）和增量调整的PMM通常用于非可重复脱落的临床试验的敏感性分析。这些PMM假设在插补程序中，结局的统计学行为因实验组的模式而异，但插补数据通常通过标准方法（如主要分析模型）进行分析。在多重插补（MI）推断中，当插补模型和分析模型不一致时，Rubin方差估计量通常是有偏的。本文的目的之一是量化鲁宾方差估计的偏差在控制为基础的和三角洲调整的PMM纵向连续的结果。这些PMM假设与重复测量混合效应模型（MMRM）相同的观察数据分布。我们推导出解析表达式的MI治疗效果估计和相关的鲁宾方差在这些PMM和MMRM的最大似然估计的功能，从MMRM分析和观察到的比例时，在每个脱落模式的插补数是无限的。在增量调整的PMM中，渐近偏差通常很小或可以忽略不计，但在基于控制的PMM中可能相当大。这表明基于Rubin规则的推断在增量调整的PMM中近似有效。提出了一个简单的方差估计，以确保渐近有效的MI推断在这些PMM，并与自助方差。所提出的方法说明了抗抑郁药试验的分析，其性能进一步评估通过模拟研究。
Control-based pattern mixture models (PMM) and delta-adjusted PMMs are commonly used as sensitivity analyses in clinical trials with non-ignorable dropout. These PMMs assume that the statistical behavior of outcomes varies by pattern in the experimental arm in the imputation procedure, but the imputed data are typically analyzed by a standard method such as the primary analysis model. In the multiple imputation (MI) inference, Rubin's variance estimator is generally biased when the imputation and analysis models are uncongenial. One objective of the article is to quantify the bias of Rubin's variance estimator in the control-based and delta-adjusted PMMs for longitudinal continuous outcomes. These PMMs assume the same observed data distribution as the mixed effects model for repeated measures (MMRM). We derive analytic expressions for the MI treatment effect estimator and the associated Rubin's variance in these PMMs and MMRM as functions of the maximum likelihood estimator from the MMRM analysis and the observed proportion of subjects in each dropout pattern when the number of imputations is infinite. The asymptotic bias is generally small or negligible in the delta-adjusted PMM, but can be sizable in the control-based PMM. This indicates that the inference based on Rubin's rule is approximately valid in the delta-adjusted PMM. A simple variance estimator is proposed to ensure asymptotically valid MI inferences in these PMMs, and compared with the bootstrap variance. The proposed method is illustrated by the analysis of an antidepressant trial, and its performance is further evaluated via a simulation study.