A general semiparametric Bayesian discrete-time recurrent events model

被引:2
作者
King, Adam J. [1 ]
Weiss, Robert E. [2 ]
机构
[1] Calif State Polytech Univ Pomona, Dept Math & Stat, 3801 West Temple Ave, Pomona, CA 91768 USA
[2] Univ Calif Los Angeles, Fielding Sch Publ Hlth, Dept Biostat, 650 Charles E Young Dr South, Los Angeles, CA 90095 USA
关键词
Competing risks; Cox model; Discrete time; Generalized additive models; Recurrent events; Semiparametric models; Software; Substance abuse; LIKELIHOOD;
D O I
10.1093/biostatistics/kxz029
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Event time variables are often recorded in a discrete fashion, especially in the case of patient-reported outcomes. This work is motivated by a study of illicit drug users, in which time to drug use cessation has been recorded as a number of whole months. Existing approaches for handling such discrete data include treating the survival times as continuous (with adjustments for inevitable tied outcomes), or using discrete models that omit important features like random effects. We provide a general Bayesian discrete-time proportional hazards model, incorporating a number of features popular in continuous-time models such as competing risks and frailties. Our model also provides flexible baseline hazards for time effects, as well as generalized additive models style semiparametric incorporation of other time-varying covariates. Our specific modeling choices enable efficient Markov chain Monte Carlo inference algorithms, which we provide to the user in the form of a freely available R package called brea. We demonstrate that our model performs better on our motivating substance abuse application than existing approaches. We also present a reproducible application of the brea software to a freely available data set from a clinical trial of anesthesia administration methods.
引用
收藏
页码:266 / 282
页数:17
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