In this paper we propose a class of multistate models for the analysis of multitype recurrent event and failure time data when there are past event feedbacks in longitudinal biomarkers. It can well incorporate various effects, including time-dependent and time-independent effects, of different event paths or the number of occurrences of events of different types. Asymptotic unbiased estimating equations based on polynomial splines approximation are developed. The consistency and asymptotic normality of the proposed estimators are provided. Simulation studies show that the naive estimators which either ignore the past event feedback or the measurement errors are biased. Our method has a better coverage probability of the time-varying/constant coefficients, compared to the naive methods. An application to the dataset from the Atherosclerosis Risk in Communities Study, which is also the motivating example to develop the method, is presented.

中文翻译：

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生物标志物中具有事件反馈的多类型复发事件和故障时间数据的多状态分析

在本文中，我们提出了一类多状态模型，用于在纵向生物标志物中存在过去事件反馈时分析多类型复发事件和故障时间数据。它可以很好地结合不同事件路径或不同类型事件发生次数的各种影响，包括时间相关和时间无关的影响。开发了基于多项式样条逼近的渐近无偏估计方程。提供了所提出的估计量的一致性和渐近正态性。模拟研究表明，忽略过去事件反馈或测量误差的幼稚估计器是有偏差的。与朴素方法相比，我们的方法具有更好的时变/常数系数覆盖概率。