Threshold Regression for Survival Analysis: Modeling Event Times by a Stochastic Process Reaching a Boundary

Details

• Personal Author:
  Lee M-LT ; Whitmore GA
• Description:
  Many researchers have investigated first hitting times as models for survival data. First hitting times arise naturally in many types of stochastic processes, ranging from Wiener processes to Markov chains. In a survival context, the state of the underlying process represents the strength of an item or the health of an individual. The item fails or the individual experiences a clinical endpoint when the process reaches an adverse threshold state for the first time. The time scale can be calendar time or some other operational measure of degradation or disease progression. In many applications, the process is latent (i.e., unobservable). Threshold regression refers to first-hitting-time models with regression structures that accommodate covariate data. The parameters of the process, threshold state and time scale may depend on the covariates. This paper reviews aspects of this topic and discusses fruitful avenues for future research. [Description provided by NIOSH]
• Subjects:
  Longevity Mortality Occupational Exposure Occupational Health Safety
• Keywords:
  Author Keywords: Accelerated Testing Calendar Time Competing Risk Cure Rate Duration Environmental Studies First Hitting Time Gamma Process Latent Variable Models Lifespan Lifetime Maximum Likelihood Models Mortality Data Occupational Exposure Operational Time Ornstein-Uhlenbeck Process Poisson Process Running Time Statistical Analysis Stochastic Process Stopping Time Survival Analysis Survival Rate Threshold Regression Time-to-event Wiener Diffusion Process

  More +
• ISSN:
  0883-4237
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Ohio ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  21
• Issue:
  4
• NIOSHTIC Number:
  nn:20058656
• Citation:
  Stat Sci 2006 Nov; 21(4):501-503
• Contact Point Address:
  Mei-Ling Ting Lee. Professor and Chair, Division of Biostatistics, School of Public Health, Ohio State University, Columbus, Ohio 43210, USA
• Email:
  meilinglee@sph.osu.edu
• Federal Fiscal Year:
  2007
• NORA Priority Area:
  Transportation, Warehousing and Utilities
• Performing Organization:
  Ohio State University
• Peer Reviewed:
  True
• Start Date:
  20060901
• Source Full Name:
  Statistical Science
• End Date:
  20090831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:e289bdf496bc41b76ff997d49135496e5705679a7927dbdd7cba8886ecb78ab2ebed55f1b578f887d0787b6e151ee4f2e6eb4a9105d74420d0160b75097c78aa
• Download URL:
  https://stacks.cdc.gov/view/cdc/221696/cdc_221696_DS1.pdf
• File Type:
  [PDF - 198.45 KB ]