A Goodness-of-Fit Test of Logistic Regression Models for Case-Control Data with Measurement Errors

• Activity Number: 139
• Type: Contributed
• Date/Time: Monday, August 1, 2011 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Ganggang Xu*+ and Suojin Wang
• Companies: Texas A & M University and Texas A & M University
• Keywords: Case-control study ; Conditional score ; Empirical likelihood ; Logistic regression ; Measurement error

Abstract

We study the problem of goodness-of-fit tests for logistic regression models for case-control data when some covariates are measured with errors.We first study the applicability of traditional test methods for this problem by simply ignoring measurement errors and show that in some scenarios they are still effective despite the inconsistency of the parameter estimators. We then develop a test procedure based on Zhang (2001) that can simultaneously test the validity of using logistic regression and correct the bias in parameter estimators for case-control data with nondifferential classical additive normal measurement error. Instead of using the information matrix considered by Zhang (2001), our test statistic uses a collection of preselected functions to reduce dimensionality. Simulation studies and an application are carried out to illustrate the usefulness of the test.