Bayesian Hierarchical Bias Model for Demonstrating Biosimilarity

• Activity Number: 245
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 2:00 PM to 2:45 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Joseph Wu*+ and Sandeep Menon and Gheorghe Doros and Mark Chang and Kerry Barker
• Companies: Boston University School of Public Health and Pfizer and Boston University School of Public Health and AMAG Pharmaceuticals and Pfizer
• Keywords: Biosimilarity ; Bayesian ; Non-inferiority ; Rheumatoid arthritis ; Composite endpoint

Abstract

Traditional statistical methods used to test for average bioequivalence as in a generic drug development may not be the most efficient ways to apply to biosimilarity. We adopt a Bayesian approach to establish biosimilarity for a composite endpoint. Specifically, we propose a hierarchical bias model to capture the effect difference between the reference and follow-on products. Within a non-inferiority framework, we formulate a statistical test using the posterior distributions to demonstrate biosimilarity. We illustrate this proposed methodology using a recombinant polypeptide example used to treat rheumatoid arthritis and the composite endpoint of ACR20. Using simulation, we have shown that the Bayesian type 1 error is preserved even when reference product is performing worse in current trial than historical trial but not the frequentist type 1 error. Statistical power is better than the frequentist approach as sample size increases.