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Lifetime Data Analysis

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25-03-2021

The semiparametric accelerated trend-renewal process for recurrent event data

Authors: Chien-Lin Su, Russell J. Steele, Ian Shrier

Published in: Lifetime Data Analysis | Issue 3/2021

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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.

previous article Information measures and design issues in the study of mortality deceleration: findings for the gamma-Gompertz model

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Title: The semiparametric accelerated trend-renewal process for recurrent event data
Authors: Chien-Lin Su
Russell J. Steele
Ian Shrier
Publication date: 25-03-2021
Publisher: Springer US
Published in: Lifetime Data Analysis / Issue 3/2021
Print ISSN: 1380-7870
Electronic ISSN: 1572-9249
DOI: https://doi.org/10.1007/s10985-021-09519-3

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