Parametric Regression Analysis with Covariate Misclassification in Main Study/Validation Study Designs

Yi Grace Y. (), Yan Ying, Liao Xiaomei and Spiegelman Donna ()
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Yi Grace Y.: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, CanadaN2L 3G1
Yan Ying: Department of Statistical Science, School of Mathematics, Sun Yat-sen University, Guangzhou, China
Liao Xiaomei: Departments of Epidemiology and Biostatistics, Harvard School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA
Spiegelman Donna: Departments of Epidemiology and Biostatistics, Harvard School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA; Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510

The International Journal of Biostatistics, 2019, vol. 15, issue 1, 24

Abstract: Measurement error and misclassification have long been a concern in many fields, including medicine, administrative health care data, epidemiology, and survey sampling. It is known that measurement error and misclassification may seriously degrade the quality of estimation and inference, and should be avoided whenever possible. However, in practice, it is inevitable that measurements contain error for a variety of reasons. It is thus necessary to develop statistical strategies to cope with this issue. Although many inference methods have been proposed in the literature to address mis-measurement effects, some important issues remain unexplored. Typically, it is generally unclear how the available methods may perform relative to each other. In this paper, capitalizing on the unique feature of discrete variables, we consider settings with misclassified binary covariates and investigate issues concerning covariate misclassification; our development parallels available strategies for handling measurement error in continuous covariates. Under a unified framework, we examine a number of valid inferential procedures for practical settings where a validation study, either internal or external, is available besides a main study. Furthermore, we compare the relative performance of these methods and make practical recommendations.

Keywords: efficiency; estimating function; external validation study; internal validation study; likelihood method; measurement error; misclassification (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1515/ijb-2017-0002

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