Semiparametric additive marginal regression models for multiple type recurrent events.

TitleSemiparametric additive marginal regression models for multiple type recurrent events.
Publication TypeJournal Article
Year of Publication2012
AuthorsChen, Xiaolin, Qihua Wang, Jianwen Cai, and Viswanathan Shankar
JournalLifetime Data Anal
Volume18
Issue4
Pagination504-27
Date Published2012 Oct
ISSN1572-9249
KeywordsBiomedical Research, Computer Simulation, Data Interpretation, Statistical, Humans, India, Infections, Kidney Transplantation, Models, Statistical, Recurrence, Regression Analysis
Abstract

Recurrent event data are often encountered in biomedical research, for example, recurrent infections or recurrent hospitalizations for patients after renal transplant. In many studies, there are more than one type of events of interest. Cai and Schaube (Lifetime Data Anal 10:121-138, 2004) advocated a proportional marginal rate model for multiple type recurrent event data. In this paper, we propose a general additive marginal rate regression model. Estimating equations approach is used to obtain the estimators of regression coefficients and baseline rate function. We prove the consistency and asymptotic normality of the proposed estimators. The finite sample properties of our estimators are demonstrated by simulations. The proposed methods are applied to the India renal transplant study to examine risk factors for bacterial, fungal and viral infections.

DOI10.1007/s10985-012-9226-4
Alternate JournalLifetime Data Anal
Original PublicationSemiparametric additive marginal regression models for multiple type recurrent events.
PubMed ID22899088
PubMed Central IDPMC3844629
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 HL057444 / HL / NHLBI NIH HHS / United States
Project: