Abstract

Reference-based controlled imputations have become a popular tool to assess the sensitivity of primary analysis inference to different post-dropout assumptions and to yield an alternative effectiveness estimand of treatment effect. As the imputation and analysis models are uncongenial in this setting, Rubin’s variance estimator overestimates the repeated sampling variability of the multiple imputation estimator of treatment effect. On the other hand, since reference-based methods borrow information across arms, the sampling variance of treatment effect estimator decreases as the proportion of missing data increases, rendering it inappropriate for use in sensitivity analysis. We propose to decouple the variance estimation from information borrowing by using a delta-adjusted pattern mixture model with delta-adjustments fixed at the maximum likelihood estimate. Reference-based pattern-mixture models can thus be embedded in the sensitivity analysis using delta-adjusted pattern-mixture models by identifying a scalar sensitivity parameter value to match the treatment effect estimate. We provide both theoretical and empirical justifications of the proposed approach and illustrate its use in the analysis of a clinical trial of major depressive disorder.

摘要

基于参考的受控插补已成为评估主要分析推断对不同辍学后假设的敏感性并产生治疗效果的替代有效性估计值的流行工具。由于插补和分析模型在这种情况下不一致，鲁宾的方差估计量高估了处理效果的多重插补估计量的重复抽样变异性。另一方面，由于基于参考的方法跨臂借用信息，治疗效果估计量的抽样方差随着缺失数据比例的增加而减小，使其不适用于敏感性分析。我们建议通过使用增量调整模式混合模型将方差估计与信息借用解耦，增量调整固定在最大似然估计上。因此，通过识别标量灵敏度参数值以匹配治疗效果估计值，可以将基于参考的模式混合模型嵌入到使用 delta 调整的模式混合模型的敏感性分析中。我们提供了所提出方法的理论和经验依据，并说明了其在重度抑郁症临床试验分析中的应用。