Pre-Conference Course 2
Evidence Synthesis for Clinical Trials: Use of Historical Data and Extrapolation - Methods, applications and implementation with the R package RBesT
Presented by Sebastian Weber and Satrajit Roychoudhury. Location: Park Plaza, Waterloo
There is an intrinsic interest of leveraging all available information for an efficient design and analysis of clinical trials. The use of external data in trials are nowadays used in earlier phases of drug development (Trippa, Rosnerand Muller, 2012; French, Thomas and Wang, 2012; Hueber et al., 2012), occasionally in phase III trials (French et al., 2012), and also in special areas such as medical devices (FDA, 2010a), orphan indications (Dupont and Van Wilder, 2011) and extrapolation in pediatric studies (Berry, 1989). This allows trials with smaller sample size or with unequal randomization (more subjects on treatment than control). In this short course, we'll provide a statistical framework to use trial external evidence to better plan and/or incorporate external information into a trial.
During the first part of the course we will introduce the meta-analytic predictive (MAP) model (Neuenschwander, 2010). The MAP model is a Bayesian hierarchical model, which combines the evidence from different potentially heterogeneous sources (usually studies).
In the second part of the course we will focus on key applications of the MAP approach in biostatistics, which are (i) the derivation of informative priors from historical controls and (ii) probability of success. These applications will be demonstrated using the R package RBesT, the R Bayesian evidence synthesis tools, which are freely available from CRAN. During the second part hands-on exercises will be part of the course to enable participants to apply the presented approach themselves.