Integrated Data Analysis for Assessing Treatment Effect through Combining Information

• Activity Number: 88
• Type: Contributed
• Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Hui Quan* and Bingzhi Zhang
• Companies: Sanofi and Sanofi
• Keywords: meta-analysis ; historical data ; surrogate endpoint ; sample size calculation

Abstract

It is critical to use a precise estimate of treatment effect when drawing conclusions, evaluating benefit/risk or designing a new study. Utilization of data from all sources in an integrated data analysis/meta-analysis will help us move closer to meeting this need. Depending on the data sources and objectives, there are many approaches for integrated analyses. These include network meta-analysis, multivariate meta-analysis, model-based meta-analysis as well as methods of borrowing historical data. In this paper, we discuss these methods with additional details for implementation and interpretation. We consider information adaptive repeated cumulative meta-analyses. We also discuss how to apply three integrated analysis approaches that take into account the variability of the overall treatment effect estimate to determine sample size for a new trial. Some computation and simulation results are provided.