Analysis of error-prone survival data under additive hazards models: measurement error effects and adjustments

Ying Yan and Grace Y. Yi ()
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Ying Yan: University of Waterloo
Grace Y. Yi: University of Waterloo

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2016, vol. 22, issue 3, No 1, 342 pages

Abstract: Abstract Covariate measurement error occurs commonly in survival analysis. Under the proportional hazards model, measurement error effects have been well studied, and various inference methods have been developed to correct for error effects under such a model. In contrast, error-contaminated survival data under the additive hazards model have received relatively less attention. In this paper, we investigate this problem by exploring measurement error effects on parameter estimation and the change of the hazard function. New insights of measurement error effects are revealed, as opposed to well-documented results for the Cox proportional hazards model. We propose a class of bias correction estimators that embraces certain existing estimators as special cases. In addition, we exploit the regression calibration method to reduce measurement error effects. Theoretical results for the developed methods are established, and numerical assessments are conducted to illustrate the finite sample performance of our methods.

Keywords: Bias analysis; Bias correction estimator; Induced hazard function; Regression calibration (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10985-015-9340-1

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