Methods for Evaluating Heterogeneity in Treatment Effects in a Randomized Clinical Trial

• Activity Number: 131 - Topics in Clinical Trials
• Type: Contributed
• Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Alok Dwivedi* and Muditha Perera and Sada Nand Dwivedi and Rakesh Shukla
• Companies: Texas Tech University Health Sciences Center El Paso and Texas Tech University Health Sciences Center El Paso and All India Institute of Medical Sciences and University of Cincinnati
• Keywords: Clinical trial; Heterogeneity ; Latent Profile Analysis; Finite Mixture Model; Cluster Analysis

Abstract

Clinical trial populations often include patients with different risks of outcome of interest. Due to considerable variation in the study populations, the average treatment effect might not be generalized to all patients with varying baseline risks of outcome. Exploring the heterogeneity in treatment effect (HTE) is necessary to make optimal decisions on treatment effect based on the subject characteristics. Conventional subgroup analyses based on a single attribute does not detect HTE appropriately. Typically, multiple characteristics of the patients affect the treatment effect. A multivariable risk prediction approach has been recommended for exploring HTE by creating baseline risk groups. However, this approach has also some limitations including optimum categorization of baseline risks of outcome. Hence, we proposed some alternative methods for assessing HTE by identifying characteristics that may modify the effect of treatment on outcome. For comparison and illustration, we applied the methods on a published clinical trial data. The analytical results reveal that a direct regression approach proposed in this study may be preferred for exploring HTE in a clinical trial.