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Bayesian analysis of survival data with missing censoring indicators
Author(s) -
Brownstein Naomi C.,
Bunn Veronica,
Castro Luis M.,
Sinha Debajyoti
Publication year - 2021
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13280
Subject(s) - censoring (clinical trials) , covariate , missing data , proportional hazards model , bayesian probability , statistics , survival analysis , computer science , accelerated failure time model , econometrics , mathematics
In some large clinical studies, it may be impractical to perform the physical examination to every subject at his/her last monitoring time in order to diagnose the occurrence of the event of interest. This gives rise to survival data with missing censoring indicators where the probability of missing may depend on time of last monitoring and some covariates. We present a fully Bayesian semi‐parametric method for such survival data to estimate regression parameters of the proportional hazards model of Cox. Theoretical investigation and simulation studies show that our method performs better than competing methods. We apply the proposed method to analyze the survival data with missing censoring indicators from the Orofacial Pain: Prospective Evaluation and Risk Assessment study.