Dynamic regression with recurrent events

Recurrent events often arise in follow-up studies where a subject may experience multiple occurrences of the same event. Most regression models with recurrent events tacitly assume constant effects of covariates over time, which may not be realistic in practice. To address time-varying effects, we d...

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Bibliographic Details
Published inBiometrics Vol. 75; no. 4; pp. 1264 - 1275
Main Authors Soh, J. E., Huang, Yijian
Format Journal Article
LanguageEnglish
Published United States Wiley Periodicals, Inc 01.12.2019
Blackwell Publishing Ltd
Subjects
BIOMETRIC METHODOLOGY
biometry
Computer simulation
counting process
event history analysis
marginal modeling
multiplier bootstrap
multivariate survival data
Regression analysis
Regression models
varying‐effects model
multivariate survival data
marginal modeling
multiplier bootstrap
event history analysis
counting process
varying-effects model
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Summary:Recurrent events often arise in follow-up studies where a subject may experience multiple occurrences of the same event. Most regression models with recurrent events tacitly assume constant effects of covariates over time, which may not be realistic in practice. To address time-varying effects, we develop a dynamic regression model to target the mean frequency of recurrent events. We propose an estimation procedure which fully exploits observed data. Consistency and weak convergence of the proposed estimator are established. Simulation studies demonstrate that the proposed method works well, and two real data analyses are presented for illustration.
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ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/biom.13105