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A Bayesian analysis of doubly censored data using a hierarchical Cox model
Journal article   Open access   Peer reviewed

A Bayesian analysis of doubly censored data using a hierarchical Cox model

Wei Zhang, Kathryn Chaloner, Mary Kathryn Cowles, Ying Zhang and Jack T Stapleton
Statistics in medicine, Vol.27(4), pp.529-542
02/20/2008
DOI: 10.1002/sim.3002
PMCID: PMC7476730
PMID: 17694594
url
https://www.ncbi.nlm.nih.gov/pmc/articles/7476730View
Open Access

Abstract

Two common statistical problems in pooling survival data from several studies are addressed. The first problem is that the data are doubly censored in that the origin is interval censored and the endpoint event may be right censored. Two approaches to incorporate the uncertainty of interval-censored origins are developed, and then compared with more usual analyses using imputation of a single fixed value for each origin. The second problem is that the data are collected from multiple studies and it is likely that heterogeneity exists among the study populations. A random-effects hierarchical Cox proportional hazards model is therefore used. The scientific problem motivating this work is a pooled survival analysis of data sets from three studies to examine the effect of GB virus type C (GBV-C) coinfection on survival of HIV-infected individuals. The time of HIV infection is the origin and for each subject this time is unknown, but is known to lie later than the last time at which the subject was known to be HIV negative, and earlier than the first time the subject was known to be HIV positive. The use of an approximate Bayesian approach using the partial likelihood as the likelihood is recommended because it more appropriately incorporates the uncertainty of interval-censored HIV infection times.
 HIV Infections Data Interpretation, Statistical Humans Bayes Theorem Proportional Hazards Models Survival Analysis GB virus C Cohort Studies

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