Adjusting for misclassification in a stratified biomarker clinical trial

Abstract

Clinical trials utilizing predictive biomarkers have become a research focus in personalized medicine. We investigate the effects of biomarker misclassification on the design and analysis of stratified biomarker clinical trials. For a variety of inference problems including marker-treatment interaction in particular, we show that marker misclassification may have profound adverse effects on the coverage of confidence intervals, power of the tests, and required sample sizes. For each inferential problem, we propose methods to adjust for the classification errors.

Keywords: biomarkers; classification error; correction for error; personalized medicine; power and sample size; prevalence; randomized controlled clinical trials; sensitivity and specificity.