Bayesian Random-Effects Threshold Regression with Application to Survival Data with Nonproportional Hazards

Details

• Personal Author:
  Lee M-LT ; Pennell ML ; Whitmore GA
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Environmental Monitoring Mortality Neoplasms Occupational Health Safety Skin Skin Diseases Workplace
• Keywords:
  Analytical-methods Analytical-processes Author Keywords: Bayesian Methodology Cancer-rates Exposure-assessment Exposure-levels Exposure-methods Mathematical-models Mortality-surveys Nonproportional Hazards Random Effects Skin-cancer Statistical-analysis Survival Analysis Threshold-limit-values Threshold Regression Time-weighted-average-exposure Wiener Process Workplace-studies

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• ISSN:
  1465-4644
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Maryland ; Ohio ; OSHA Region 3 ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  111-126
• Volume:
  11
• Issue:
  1
• NIOSHTIC Number:
  nn:20036310
• Citation:
  Biostatistics 2010 Jan; 11(1):111-126
• Contact Point Address:
  Michael L. Pennell, Division of Biostatistics, College of Public Health, The Ohio State University, 320 West 10th Avenue, Columbus, OH 43210
• Email:
  mpennell@cph.osu.edu
• Federal Fiscal Year:
  2010
• NORA Priority Area:
  Transportation, Warehousing and Utilities
• Performing Organization:
  University of Maryland - College Park
• Peer Reviewed:
  True
• Start Date:
  20060901
• Source Full Name:
  Biostatistics
• End Date:
  20090831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:a5d1bdcb2c29166b1b7b068aa0686ebdc47e23c673b4c57582f97064c1eb0d20a614912e36f4004fcb664ac069ee5b55f2f3cb44d04af0669d856cb481eccf67
• Download URL:
  https://stacks.cdc.gov/view/cdc/187785/cdc_187785_DS1.pdf
• File Type:
  [PDF - 241.08 KB ]