Selection Bias in Adaptive Enrichment Designs

• Activity Number: 231
• Type: Topic Contributed
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Noah Simon*
• Companies: University of Washington
• Keywords: adaptive ; enrichment ; clinical trial ; selection bias

Abstract

The biomedical field has recently focused on developing targeted therapies, designed to be effective in only some subset of the population with a given disease. However, for many new treatments, characterizing this subset has been a challenge --- often there is insufficient information until well into large-scale trials. To combat this, statisticians have been developing adaptive enrichment designs: clinical trial designs that allow the simultaneous construction and use of a biomarker, during an ongoing trial, to adaptively enrich the enrolled population.

For poorly characterized biomarkers, these trials can significantly improve power while still controlling type one error. However, these trials have challenges of their own. Estimating treatment effect-size in the selected population is principle among these challenges. The selected population was chosen precisely because it was the population for which treatment showed a large empirical effect; thus the naïve effect-size estimate will be anti-conservatively biased. In this talk we will discuss the severity of this bias; we will propose an approach to estimate the bias, and show how it performs in simulation.