Integrated Data Analysis for Assessing Treatment Effect through Combining Information from All Sources
Christy Chuang-Stein, Chuang-Stein Consulting  Byron Jones, Novartis  *Hui Quan, sanofi  Bingzhi Zhang, Sanofi 

Keywords: meta-analysis, historical data, surrogate endpoint, sample size calculation

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 details for implementation and interpretation. In addition, we consider information adaptive repeated cumulative meta-analyses. We also discuss how to apply three approaches that take into account the variability of the overall treatment effect estimate obtained through integrated analysis to determine sample size for a new trial. Some computation and simulation results are provided.