KLATASDS-MOE, Academy of Statistics and Interdisciplinary Sciences, East China Normal University, Shanghai, China.
School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China.
Stat Med. 2022 Sep 30;41(22):4285-4298. doi: 10.1002/sim.9509. Epub 2022 Jun 28.
Multivariate recurrent event data are frequently encountered in biomedical and epidemiological studies when subjects experience multiple types of recurrent events. In practice, the event type information may be missing due to a variety of reasons. In this article, we consider a semiparametric additive rates model for multivariate recurrent event data with missing event types. We develop the augmented inverse probability weighting technique to handle event types that are missing at random. The nonparametric kernel-assisted proposals for the missing mechanisms are studied. The resulting estimator is shown to be consistent and asymptotically normal. Extensive simulation studies and a real data application are provided to illustrate the validity and practical utility of the proposed method.
多变量复发事件数据在生物医学和流行病学研究中经常遇到，当研究对象经历多种类型的复发事件时。在实践中，由于各种原因，事件类型信息可能会缺失。在本文中，我们考虑了一种用于具有缺失事件类型的多变量复发事件数据的半参数加性速率模型。我们开发了扩充逆概率加权技术来处理随机缺失的事件类型。研究了缺失机制的非参数核辅助建议。所得到的估计量被证明是一致的和渐近正态的。提供了广泛的模拟研究和实际数据应用，以说明所提出方法的有效性和实际效用。
