The semiparametric accelerated trend-renewal process for recurrent event data

Chien-Lin Su (), Russell J. Steele () and Ian Shrier ()
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Chien-Lin Su: McGill University
Russell J. Steele: McGill University
Ian Shrier: McGill University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2021, vol. 27, issue 3, No 2, 357-387

Abstract: 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.

Keywords: Accelerated transformed gap time model; Buckley–James imputation; Model diagnostic plots; Prediction; Recurrent events; Trend-renewal process (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10985-021-09519-3

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