A Conditional Likelihood Approach for Regression Analysis Using Biomarkers Measured with Batch-Specific Error

• Activity Number: 42
• Type: Contributed
• Date/Time: Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Ming Wang*+ and W. Dana Flanders and Roberd M. Bostick and Qi Long
• Companies: Emory University and Emory University and Emory Winship Cancer Institute and Emory University
• Keywords: Conditional likelihood ; Exponential family ; Batch-specific error ; Generalized linear models ; Biomaker

Abstract

Measurement error is common in epidemiological and biomedical studies. When biomarkers are measured in batches or groups, measurement error is potentially correlated within each batch or group, where most existing methods are not applicable. We propose a robust conditional likelihood approach to account for batch-specific error in predictors when batch effect is additive and the predominant source of error, which requires no assumptions on the distribution of measurement error. Our simulation studies show that the conditional likelihood approach achieves better finite sample performance than the regression calibration approach as well as a naive approach without adjustment for measurement error. In the case of logistic regression, our proposed approach is shown to also outperform the regression approach with batch as a categorical covariate. In addition, we also examine a "hybrid" approach combining the conditional likelihood method and the regression calibration method, which is shown in simulations to achieve good performance in the presence of both batch-specific and measurement-specic error. We illustrate our method using data from a colorectal adenoma study.