Logic-Respecting Efficacy Measures in the Presence of Prognostic or Predictive Biomarker Subgroups

• Activity Number: 409 - Estimands in Oncology: Papers from an Industry Working Group
• Type: Topic-Contributed
• Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Yi Liu*
• Companies: Nektar Therapeutic
• Keywords: subgroup; odds ratio; hazard ratio; time-to-event

Abstract

In the development of precision medicine, understanding treatment effect in biomarker subgroups and its relation to the overall population is essential. For continuous outcomes, Least Square estimates from the full model containing treatment, subgroup, and its interaction term enable an unbiased estimation of treatment effect for the overall population by linearly combining treatment effects of the two subgroups. Such logic is currently carried to binary and time-to-event outcomes models in most statistical software where model parameters are linearly combined in the log scale and then exponentiated to represent treatment effect in the overall population. Although guaranteeing logical inference in appearance, such calculations do not correspond to the true overall treatment effect which may in fact be illogical for efficacy measures such as odds ratio and hazard ratio. To correctly derive efficacy in the overall population, a principle called Subgroup Mixable Estimation (SME) should be followed. We illustrate these common mistakes and demonstrate the application of SME using real trial data.