Simulation Extrapolation Method for Cox Regression Model with a Mixture of Berkson and Classical Errors in the Covariates using Calibration Data

Tapsoba Jean de Dieu (), Chao Edward C. and Wang Ching-Yun
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Tapsoba Jean de Dieu: Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A.
Chao Edward C.: Data Numerica Institute, Bellevue, Washington 98006, U.S.A.
Wang Ching-Yun: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, U.S.A.

The International Journal of Biostatistics, 2019, vol. 15, issue 2, 17

Abstract: Many biomedical or epidemiological studies often aim to assess the association between the time to an event of interest and some covariates under the Cox proportional hazards model. However, a problem is that the covariate data routinely involve measurement error, which may be of classical type, Berkson type or a combination of both types. The issue of Cox regression with error-prone covariates has been well-discussed in the statistical literature, which has focused mainly on classical error so far. This paper considers Cox regression analysis when some covariates are possibly contaminated with a mixture of Berkson and classical errors. We propose a simulation extrapolation-based method to address this problem when two replicates of the mismeasured covariates are available along with calibration data for some subjects in a subsample only. The proposed method places no assumption on the mixture percentage. Its finite-sample performance is assessed through a simulation study. It is applied to the analysis of data from an AIDS clinical trial study.

Keywords: Berkson error; classical error; instrumental variable; proportional hazards model; simulation extrapolation (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/ijb-2018-0028

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