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<p>In clinical and observational research, multivariate recurrent event data are commonplace, as people may encounter several kinds of repeating occurrences. While event times are consistently recorded, there are instances where the corresponding event types might not be observed. For multivariate recurrent event data, the authors introduce a time-varying coefficients additive rate model which takes into consideration scenarios in which event types are missing at random (MAR). The authors employ an inverse probability-weighted estimating equation to derive inferences for the time-independent and time-dependent effects, with proofs for the estimators’ asymptotic behavior. Moreover, the authors offer statistical tests to assess the temporal variation of covariate effects. The estimators’ performance is evaluated by simulation experiments, and the authors apply this method to a platelet transfusion reactions dataset.</p>

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Additive Rates Model for Multivariate Recurrent Event Data with Time-Dependent Coefficients and Missing Types

  • 期刊论文

  • 发表日期：

    2025年6月9日

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  • Xiaowei Sun,
  • Jiaqi Chen

摘要

In clinical and observational research, multivariate recurrent event data are commonplace, as people may encounter several kinds of repeating occurrences. While event times are consistently recorded, there are instances where the corresponding event types might not be observed. For multivariate recurrent event data, the authors introduce a time-varying coefficients additive rate model which takes into consideration scenarios in which event types are missing at random (MAR). The authors employ an inverse probability-weighted estimating equation to derive inferences for the time-independent and time-dependent effects, with proofs for the estimators’ asymptotic behavior. Moreover, the authors offer statistical tests to assess the temporal variation of covariate effects. The estimators’ performance is evaluated by simulation experiments, and the authors apply this method to a platelet transfusion reactions dataset.