Standard and reference-based conditional mean imputation.
Standard and reference-based conditional mean imputation.
复制标题
DOI：
10.1002/pst.2234
复制
发表时间：
2022-11
影响因子：
1.5
通讯作者：
Bartlett, Jonathan W.
中科院分区：
文献类型：
--
作者：
Wolbers, Marcel;Noci, Alessandro;Delmar, Paul;Gower-Page, Craig;Yiu, Sean;Bartlett, Jonathan W.
具有纵向结果的临床试验通常包括由于错过评估或在用假设策略处理同时发生的事件后结果的结构性缺失而丢失的数据。为了使这些试验的分析与目标估计一致，越来越多地使用基于贝叶斯随机多重推算和鲁宾规则的方法来跨多个推算数据集汇集结果。我们提出并证明了确定性条件均值推论与折刀推理相结合作为一种替代方法的合理性。该方法适用于随机缺失假设下的推算，也适用于基于参考的推算方法。在应用和模拟研究中，我们证明了它提供了一致的贝叶斯方法的治疗效果估计和可靠的频率推断，具有准确的标准误差估计和第I类误差控制。该方法的另一个优点是它不依赖于随机抽样，因此是可复制的，不受蒙特卡罗误差的影响。
Clinical trials with longitudinal outcomes typically include missing data due to missed assessments or structural missingness of outcomes after intercurrent events handled with a hypothetical strategy. Approaches based on Bayesian random multiple imputation and Rubin's rules for pooling results across multiple imputed data sets are increasingly used in order to align the analysis of these trials with the targeted estimand. We propose and justify deterministic conditional mean imputation combined with the jackknife for inference as an alternative approach. The method is applicable to imputations under a missing‐at‐random assumption as well as for reference‐based imputation approaches. In an application and a simulation study, we demonstrate that it provides consistent treatment effect estimates with the Bayesian approach and reliable frequentist inference with accurate standard error estimation and type I error control. A further advantage of the method is that it does not rely on random sampling and is therefore replicable and unaffected by Monte Carlo error.
登录
查看更多内容
影响因子：
--
作者：
Mallinckrodt, Craig H.;Lane, Peter W.;Mancuso, James P.
通讯作者：
Mancuso, James P.
影响因子：
5.7
作者：
MENG, XL
通讯作者：
MENG, XL
影响因子：
1.8
作者：
Guizzaro, Lorenzo;Petavy, Frank;Gallo, Ciro
通讯作者：
Gallo, Ciro
影响因子：
1.9
作者：
Tang, Yongqiang
通讯作者：
Tang, Yongqiang
影响因子：
4.5
作者：
EFRON, B;STEIN, C
通讯作者：
STEIN, C