The Use of Subgroup Meta-Analysis to Support Decision Making of Prescription Label Change

• Activity Number: 77 - Contributed Poster Presentations: Biopharmaceutical Section
• Type: Contributed
• Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Mehreteab Aregay* and Baldur Magnusson and Gregory Ligozio
• Companies: Novartis Pharmaceuticals Corporation and Novartis Pharmaceuticals Corporation and Novartis Pharmaceuticals Corporation
• Keywords: Subgroup analysis; Meta analysis; Predicted OR; Bayesian ; logistic regression; Posterior Probabilities

Abstract

One of the research questions in clinical trials is that can we identify subgroup of patients that may respond adequately to different dose levels of treatment. Some patients may respond adequately to a lower dose whereas other subgroup of patients may respond well at a higher dose level. Based on the characteristics of the patients, the label on the drug could be different. The label on the drug may be to start at the lower dose and then to increase into the higher dose if the patient did not respond adequately at the lower dose level. Alternatively, the label could be to use the higher dose for severely sick patients otherwise to use the lower dose for not severely disease patients. Hence, the questions from the clinicians is that should the posology in the label be updated based on new information? To answer those kind of questions, we considered a Bayesian logistic regression meta-analysis and assess the posterior predicted ORs (higher vs. lower dose) and response probabilities. Hence, we investigated 10 potential subgroups including weight, gender, and found consistent difference in magnitude of response, favoring higher dose over lower dose across subgroups and overall.