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Augmented weighting estimators for the additive rates model under multivariate recurrent event data with missing
Huijuan Ma1, Weicai Pang2, Liuquan Sun3
1KLATASDS-MOE, Academy of Statistics and Interdisciplinary Sciences, East China Normal University, Shanghai, China.
Abstract:
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.
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