Wang Chia-Ning, Little Roderick, Nan Bin, Harlow Siobán D
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA.
Biometrics. 2011 Dec;67(4):1573-82. doi: 10.1111/j.1541-0420.2011.01558.x. Epub 2011 Mar 1.
We propose a regression-based hot-deck multiple imputation method for gaps of missing data in longitudinal studies, where subjects experience a recurrent event process and a terminal event. Examples are repeated asthma episodes and death, or menstrual periods and menopause, as in our motivating application. Research interest concerns the onset time of a marker event, defined by the recurrent event process, or the duration from this marker event to the final event. Gaps in the recorded event history make it difficult to determine the onset time of the marker event, and hence, the duration from onset to the final event. Simple approaches such as jumping gap times or dropping cases with gaps have obvious limitations. We propose a procedure for imputing information in the gaps by substituting information in the gap from a matched individual with a completely recorded history in the corresponding interval. Predictive mean matching is used to incorporate information on longitudinal characteristics of the repeated process and the final event time. Multiple imputation is used to propagate imputation uncertainty. The procedure is applied to an important data set for assessing the timing and duration of the menopausal transition. The performance of the proposed method is assessed by a simulation study.
我们提出了一种基于回归的热卡多重填补方法，用于处理纵向研究中缺失数据的缺口，其中研究对象经历了一个复发事件过程和一个终末事件。例如，在我们的激励性应用中，重复的哮喘发作和死亡，或月经周期和绝经。研究兴趣在于由复发事件过程定义的标记事件的发生时间，或从该标记事件到最终事件的持续时间。记录的事件历史中的缺口使得难以确定标记事件的发生时间，因此也难以确定从发病到最终事件的持续时间。诸如跳过缺口时间或剔除有缺口的病例等简单方法有明显的局限性。我们提出了一种通过用在相应区间内具有完整记录历史的匹配个体的缺口信息来替代缺口信息，从而填补缺口中信息的程序。预测均值匹配用于纳入关于重复过程的纵向特征和最终事件时间的信息。多重填补用于传播填补的不确定性。该程序应用于一个重要的数据集，以评估绝经过渡的时间和持续时间。通过模拟研究评估了所提出方法的性能。
