Valid Adjustment of Randomized Comparisons for Binary Covariates

2004 ◽  
Vol 46 (5) ◽  
pp. 589-594 ◽  
Author(s):  
Vance W. Berger
Keyword(s):  
Binary Covariates

Improving measurement of binary covariates in claims data: A simulation study

2020 ◽  
Vol 29 (9) ◽  
pp. 1093-1100
Author(s):  
John G. Connolly ◽  
Robert J. Glynn ◽  
Sebastian Schneeweiss ◽  
Joshua J. Gagne
Keyword(s):  
Simulation Study ◽  
Claims Data ◽  
Binary Covariates

Causal conclusions are most sensitive to unobserved binary covariates

2006 ◽  
Vol 25 (13) ◽  
pp. 2257-2271 ◽  
Author(s):  
Liansheng Wang ◽  
Abba M. Krieger
Keyword(s):  
Binary Covariates

Nonparametric Adjustment Techniques for Binary Covariates

2005 ◽  
Vol 47 (2) ◽  
pp. 199-205 ◽  
Author(s):  
Vance W. Berger
Keyword(s):  
Binary Covariates

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

2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Grace Y. Yi ◽  
Ying Yan ◽  
Xiaomei Liao ◽  
Donna Spiegelman
Keyword(s):  
Measurement Error ◽  
Validation Study ◽  
Main Study ◽  
Unified Framework ◽  
Continuous Covariates ◽  
Binary Covariates ◽  
Inference Methods ◽  
Study Designs ◽  
Practical Recommendations

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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