Multistate analysis of multitype recurrent event and failure time data with event feedbacks in biomarkers
提出一类多状态模型，分析多类型复发事件和失效时间数据，考虑纵向生物标志物中过去事件的反馈效应，通过多项式样条逼近构建渐近无偏估计方程，模拟显示优于忽略反馈或测量误差的朴素方法。
Abstract 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.