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.

中文翻译：

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具有缺失事件类型的多变量复发事件数据下加性比率模型的增强加权估计量

当受试者经历多种类型的复发事件时，在生物医学和流行病学研究中经常遇到多变量复发事件数据。在实践中，由于各种原因，可能会丢失事件类型信息。在本文中，我们考虑了一种用于缺少事件类型的多元重复事件数据的半参数加性比率模型。我们开发了增强的逆概率加权技术来处理随机丢失的事件类型。研究了缺失机制的非参数内核辅助建议。结果估计量显示为一致且渐近正态。提供了广泛的模拟研究和真实数据应用来说明所提出方法的有效性和实用性。