Functional and Structural Methods With Mixed Measurement Error and Misclassification in Covariates

Abstract

Covariate measurement imprecision or errors arise frequently in many areas. It is well known that ignoring such errors can substantially degrade the quality of inference or even yield erroneous results. Although in practice both covariates subject to measurement error and covariates subject to misclassification can occur, research attention in the literature has mainly focused on addressing either one of these problems separately. To fill this gap, we develop estimation and inference methods that accommodate both characteristics simultaneously. Specifically, we consider measurement error and misclassification in generalized linear models under the scenario that an external validation study is available, and systematically develop a number of effective functional and structural methods. Our methods can be applied to different situations to meet various objectives.

• External validation study
• Functional measurement error modeling
• Generalized linear models
• Likelihood method
• Regression calibration
• Semiparametric regression
• Simulation extrapolation algorithm
• Structural measurement error modeling

Additional information

Notes on contributors

Grace Y. Yi

Grace Y. Yi, Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1 (E-mail: [email protected]).

Yanyuan Ma

Yanyuan Ma, Department of Statistics, Texas A&M University, TAMU 3143, College Station, TX 77843-3143 (E-mail: [email protected]).

Donna Spiegelman

Donna Spiegelman, Departments of Epidemiology and Biostatistics, Harvard School of Public Health, 677 Huntington Ave, Boston, MA 02115 (E-mail: [email protected] ).

Raymond J. Carroll

Raymond J. Carroll, Department of Statistics, Texas A&M University, TAMU 3143, College Station, TX 77843-3143, and School of Mathematical Sciences, University of Technology, Sydney, Broadway NSW 2007 (E-mail: [email protected] ).