Bayesian analysis under accelerated failure time models with error-prone time-to-event outcomes

Yanlin Tang (), Xinyuan Song () and Grace Yun Yi ()
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Yanlin Tang: East China Normal University
Xinyuan Song: The Chinese University of Hong Kong
Grace Yun Yi: University of Western Ontario

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2022, vol. 28, issue 1, No 7, 139-168

Abstract: Abstract We consider accelerated failure time models with error-prone time-to-event outcomes. The proposed models extend the conventional accelerated failure time model by allowing time-to-event responses to be subject to measurement errors. We describe two measurement error models, a logarithm transformation regression measurement error model and an additive error model with a positive increment, to delineate possible scenarios of measurement error in time-to-event outcomes. We develop Bayesian approaches to conduct statistical inference. Efficient Markov chain Monte Carlo algorithms are developed to facilitate the posterior inference. Extensive simulation studies are conducted to assess the performance of the proposed method, and an application to a study of Alzheimer’s disease is presented.

Keywords: AFT models; Bayesian inference; Error-prone outcome; MCMC methods; Time-to-event data (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10985-021-09543-3

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