Fully Parametric and Semi-Parametric Regression Models for Common Events with Covariate Measurement Error in Main Study/Validation Study Designs

Biometrics ◽  
1997 ◽  
Vol 53 (2) ◽  
pp. 395 ◽  
Author(s):  
Donna Spiegelman ◽  
Mario Casella
2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Grace Y. Yi ◽  
Ying Yan ◽  
Xiaomei Liao ◽  
Donna Spiegelman

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.


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