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Bayesian random-effects threshold regression with application to survival data with nonproportional hazards.

Authors
Pennell-ML; Whitmore-GA; Ting-Lee-ML
Source
Biostatistics 2010 Jan; 11(1):111-126
NIOSHTIC No.
20036310
Abstract
In epidemiological and clinical studies, time-to-event data often violate the assumptions of Cox regression due to the presence of time-dependent covariate effects and unmeasured risk factors. An alternative approach, which does not require proportional hazards, is to use a first hitting time model which treats a subject's health status as a latent stochastic process that fails when it reaches a threshold value. Although more flexible than Cox regression, existing methods do not account for unmeasured covariates in both the initial state and the rate of the process. To address this issue, we propose a Bayesian methodology that models an individual's health status as a Wiener process with subject-specific initial state and drift. Posterior inference proceeds via a Markov chain Monte Carlo methodology with data augmentation steps to sample the final health status of censored observations. We apply our method to data from melanoma patients with nonproportional hazards and find interesting differences from a similar model without random effects. In a simulation study, we show that failure to account for unmeasured covariates can lead to inaccurate estimates of survival probabilities.
Keywords
Analytical-methods; Analytical-processes; Cancer-rates; Exposure-assessment; Exposure-levels; Exposure-methods; Mathematical-models; Mortality-surveys; Skin-cancer; Statistical-analysis; Threshold-limit-values; Time-weighted-average-exposure; Workplace-studies; Author Keywords: Bayesian methodology; Nonproportional hazards; Random effects; Survival analysis; Threshold regression; Wiener process
Contact
Michael L. Pennell, Division of Biostatistics, College of Public Health, The Ohio State University, 320 West 10th Avenue, Columbus, OH 43210
Publication Date
20100101
Document Type
Journal Article
Email Address
mpennell@cph.osu.edu
Funding Type
Grant
Fiscal Year
2010
NTIS Accession No.
NTIS Price
Identifying No.
Grant-Number-R01-OH-008649
Issue of Publication
1
ISSN
1465-4644
Source Name
Biostatistics
State
MD; OH
Performing Organization
Ohio State University
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