Adjusting for Misclassification in the Design and Analysis of Stratified Biomarker Clinical Trials

• Activity Number: 234
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Susan Halabi*+ and Chen-Yen Lin and Aiyi Liu
• Companies: Duke University and Eli Lilly and Company and NICHD
• Keywords: clinical trials ; stratified design ; biomarker ; sensitivity ; misclassification ; SIMEX

Abstract

Clinical trials utilizing predictive biomarkers have become a topic of increasing research in the era of personalized medicine. We confine our attention to the stratified biomarker design where patients with the same biomarker status are randomly assigned to either an experimental arm or the standard of treatment. The primary interest of a stratified biomarker design is to investigate if patients respond differently to treatment based on their biomarker status. Despite the advancements in molecular assays, correctly identifying the biomarker status remains a challenging task. We analytically demonstrate the profound adverse effects of misclassified biomarker status on the estimates of treatment effect, biomarker effect, treatment-biomarker interaction, the corresponding confidence intervals, power of the tests, and required sample sizes. We further propose respective remedies that tackle the misclassified biomarkers in the design and analysis phase of clinical trials. We illustrate the serious consequences of ignoring the classification error and demonstrate the performance of the proposed solutions using simulations.