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The semiparametric accelerated trend-renewal process for recurrent event data

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Abstract

Recurrent event data arise in many biomedical longitudinal studies when health-related events can occur repeatedly for each subject during the follow-up time. In this article, we examine the gap times between recurrent events. We propose a new semiparametric accelerated gap time model based on the trend-renewal process which contains trend and renewal components that allow for the intensity function to vary between successive events. We use the Buckley–James imputation approach to deal with censored transformed gap times. The proposed estimators are shown to be consistent and asymptotically normal. Model diagnostic plots of residuals and a method for predicting number of recurrent events given specified covariates and follow-up time are also presented. Simulation studies are conducted to assess finite sample performance of the proposed method. The proposed technique is demonstrated through an application to two real data sets.

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Acknowledgements

The authors would like to thank the editors, the associate editor, and two anonymous referees whose comments led to a substantial improvement of this paper. Chien-Lin Su gratefully acknowledges the financial support as a STATLAB-CANSSI-CRM postdoctoral fellow (2016-2017) by the Canadian Statistical Sciences Institute (CANSSI) and Centre de recherches mathématiques (CRM) in Montreal. Russell Steele and Ian Shrier acknowledge the joint financial support of the Natural Science and Engineering Research Council (NSERC) and the Canadian Institutes for Health Research (CIHR) via a Collaborative Health Research Project Grant (Grant Number: CHRPJ/478521-2015).

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Authors and Affiliations

  1. Department of Mathematics and Statistics, McGill University, Montréal, QC, Canada

    Chien-Lin Su & Russell J. Steele

  2. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada

    Chien-Lin Su

  3. Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, QC, Canada

    Ian Shrier

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  1. Chien-Lin Su
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  2. Russell J. Steele
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Correspondence to Chien-Lin Su.

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Su, CL., Steele, R.J. & Shrier, I. The semiparametric accelerated trend-renewal process for recurrent event data. Lifetime Data Anal 27, 357–387 (2021). https://doi.org/10.1007/s10985-021-09519-3

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