论文标题
混合变量类型的多元结果丢失数据插补
Missing data imputation for a multivariate outcome of mixed variable types
论文作者
论文摘要
在临床试验中收集的数据通常由多种类型的变量组成。例如，实验室测量和生命体征是连续或分类变量的纵向数据，不良事件可能是经常发生的事件，而死亡是事件时间的变量。由于患者在研究中停用或使用假设策略处理互动事件而导致的数据几乎总是发生在任何临床试验期间。同时将这些数据同时归纳为混合的变量类型，这是尚未进行广泛研究的挑战。在本文中，我们建议使用近似的完全条件规范来估算丢失的数据。仿真显示，在随机丢失的假设下，提出的方法提供了令人满意的结果。最后，分析了来自临床试验评估糖尿病治疗方法的实际数据，以说明该方法的潜在益处。
Data collected in clinical trials are often composed of multiple types of variables. For example, laboratory measurements and vital signs are longitudinal data of continuous or categorical variables, adverse events may be recurrent events, and death is a time-to-event variable. Missing data due to patients' discontinuation from the study or as a result of handling intercurrent events using a hypothetical strategy almost always occur during any clinical trial. Imputing these data with mixed types of variables simultaneously is a challenge that has not been studied extensively. In this article, we propose using an approximate fully conditional specification to impute the missing data. Simulation shows the proposed method provides satisfactory results under the assumption of missing at random. Finally, real data from a clinical trial evaluating treatments for diabetes are analyzed to illustrate the potential benefit of the proposed method.
