Continuous Covariate Imbalance and Conditional Power for Clinical Trial Interim Analyzes

• Activity Number: 128
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Jody Ciolino*+ and Renee' Martin and Wenle Zhao and Edward Jauch and Michael Hill and Yuko Palesch
• Companies: Northwestern University and Medical University of South Carolina and Medical University of South Carolina and Medical University of South Carolina and University of Calgary and Medical University of South Carolina
• Keywords: covariate adjusted analysis ; conditional power ; covariate imbalance

Abstract

Oftentimes valid statistical analyses for clinical trials involve adjustment for known influential covariates, regardless of imbalance observed in these covariates at baseline across treatment groups. Thus, it must be the case that valid interim analyses also properly adjust for these covariates. There are situations, however, in which covariate adjustment is not possible, not planned, or simply carries less merit as it makes inferences less generalizable and less intuitive. In this case, covariate imbalance between treatment groups can have a substantial effect on both interim and final primary outcome analyses. This paper illustrates the effect of influential continuous baseline covariate imbalance on unadjusted conditional power (CP), and thus, on trial decisions based on futility stopping bounds. The robustness of the relationship is illustrated for normal, skewed, and bimodal continuous baseline covariates that are related to a normally distributed primary outcome.