Document Detail


Semiparametric analysis for recurrent event data with time-dependent covariates and informative censoring.
MedLine Citation:
PMID:  19459839     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Recurrent event data analyses are usually conducted under the assumption that the censoring time is independent of the recurrent event process. In many applications the censoring time can be informative about the underlying recurrent event process, especially in situations where a correlated failure event could potentially terminate the observation of recurrent events. In this article, we consider a semiparametric model of recurrent event data that allows correlations between censoring times and recurrent event process via frailty. This flexible framework incorporates both time-dependent and time-independent covariates in the formulation, while leaving the distributions of frailty and censoring times unspecified. We propose a novel semiparametric inference procedure that depends on neither the frailty nor the censoring time distribution. Large sample properties of the regression parameter estimates and the estimated baseline cumulative intensity functions are studied. Numerical studies demonstrate that the proposed methodology performs well for realistic sample sizes. An analysis of hospitalization data for patients in an AIDS cohort study is presented to illustrate the proposed method.
Authors:
C-Y Huang; J Qin; M-C Wang
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2009-05-12
Journal Detail:
Title:  Biometrics     Volume:  66     ISSN:  1541-0420     ISO Abbreviation:  Biometrics     Publication Date:  2010 Mar 
Date Detail:
Created Date:  2010-05-24     Completed Date:  2010-09-24     Revised Date:  2013-06-02    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  39-49     Citation Subset:  IM    
Affiliation:
Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA. huangchi@niaid.nih.gov
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Biometry / methods*
Chronic Disease / epidemiology*
Computer Simulation
Data Interpretation, Statistical*
Epidemiologic Methods*
Humans
Information Storage and Retrieval / methods*
Models, Statistical*
Recurrence
Statistical Distributions
Grant Support
ID/Acronym/Agency:
DA 04334/DA/NIDA NIH HHS; DA 08009/DA/NIDA NIH HHS; R01 AI078835/AI/NIAID NIH HHS; Z99 AI999999/AI/NIAID NIH HHS
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