All Times EDT
Virtual
Leveraging External Data in Bayesian Adaptive Designs (308418)
*Alejandra Avalos Pacheco, Harvard-MIT center for regulatory science, Harvard Medical SchoolLorenzo Trippa, Harvard School of Public Health
Steffen Ventz, Harvard School of Public Health
Keywords: Platform designs, data integration, clinical studies, Bayesian models, decision making, regulatory science
In recent years there has been a growing interest in trial designs that incorporate data from real world observational studies or from completed trials with the goal of increasing power and reduce the sample size of a trial required for a given power. I will discuss two uses of external data in the design and analyses of clinical studies. Firstly, I will introduce a novel Bayesian hybrid platform design that leverages external data via a non-parametric Bayesian model averaging approach, adjusts for confounding, and satisfies a set of required operating characteristics required by regulators. I will show the usefulness of this hybrid design using a collection of phase II and III trials of cancer immunotherapies for glioblastoma. Secondly, I will discuss validation techniques to quantify the accuracy of clinical outcome predictions obtained when leveraging external data, and compare their performance with other predictive clinical outcomes that do not incorporate such external information. I will illustrate the latter strategy with repositories of trial data.