Analysis of case-control data with interacting misclassified covariates

Case-control studies are important and useful methods for studying health outcomes and many methods have been developed for analyzing case-control data. Those methods, however, are vulnerable to mismeasurement of variables; biased results are often produced if such a feature is ignored. In this paper, we develop an inference method for handling case-control data with interacting misclassified covariates. We use the prospective logistic regression model to feature the development of the disease. To characterize the misclassification process, we consider a practical situation where replicated measurements of error-prone covariates are available. Our work is motivated in part by a breast cancer case-control study where two binary covariates are subject to misclassification. Extensions to other settings are outlined.

[1]  N. Day,et al.  Misclassification in more than one factor in a case-control study: a combination of Mantel-Haenszel and maximum likelihood approaches. , 1989, Statistics in medicine.

[2]  D Spiegelman,et al.  Matrix Methods for Estimating Odds Ratios with Misclassified Exposure Data: Extensions and Comparisons , 1999, Biometrics.

[3]  R. Pyke,et al.  Logistic disease incidence models and case-control studies , 1979 .

[4]  Joseph G Ibrahim,et al.  Estimation and inference for case-control studies with multiple non-gold standard exposure assessments: with an occupational health application. , 2009, Biostatistics.

[5]  Lesley Rushton,et al.  Robust Bayesian Sensitivity Analysis for Case–Control Studies with Uncertain Exposure Misclassification Probabilities , 2015, The international journal of biostatistics.

[6]  R. Serfling.  Approximation Theorems of Mathematical Statistics , 1980 .

[7]  N. E. Breslow.  Statistical Methods in Cancer Research , 1986 .

[8]  Ye Ye,et al.  Extended Matrix and Inverse Matrix Methods Utilizing Internal Validation Data When Both Disease and Exposure Status Are Misclassified , 2013, Epidemiologic methods.

[9]  P Gustafson,et al.  Case–Control Analysis with Partial Knowledge of Exposure Misclassification Probabilities , 2001, Biometrics.

[10]  Norman E. Breslow,et al.  Logistic regression for two-stage case-control data , 1988 .

[11]  A T Marinos,et al.  Experimental quantiles of epidemiological indices in case-control studies with non-differential misclassification. , 1995, Statistics in medicine.

[12]  R. Carroll,et al.  Prospective Analysis of Logistic Case-Control Studies , 1995 .

[13]  Robert H Lyles,et al.  A Note on Estimating Crude Odds Ratios in Case–Control Studies with Differentially Misclassified Exposure , 2002, Biometrics.

[14]  I. Bross.  Misclassification in 2 X 2 Tables , 1954 .

[15]  B A Barron,et al.  The effects of misclassification on the estimation of relative risk. , 1977, Biometrics.

[16]  James J Schlesselman.  Case-Control Studies: Design, Conduct, Analysis , 1982 .

[17]  K. Roeder,et al.  A Semiparametric Mixture Approach to Case-Control Studies with Errors in Covariables , 1996 .

[18]  Thomas J. Santner,et al.  Estimators of Odds Ratio Regression Parameters in Matched Case-Control Studies with Covariate Measurement Error , 1995 .

[19]  D. Ruppert,et al.  Measurement Error in Nonlinear Models , 1995 .

[20]  Mitchell H. Gail,et al.  Case-Control Studies With Errors in Covariates , 1993 .

[21]  Stephen Gruber,et al.  Accounting for error due to misclassification of exposures in case–control studies of gene–environment interaction , 2008, Statistics in medicine.

[22]  Paul H Garthwaite,et al.  Bayesian analysis of misclassified binary data from a matched case–control study with a validation sub‐study , 2005, Statistics in medicine.

[23]  B G Armstrong,et al.  Analysis of case-control data with covariate measurement error: application to diet and colon cancer. , 1989, Statistics in medicine.