PROGRAM - Wednesday, May 19, 2010

Although comparative effectiveness research (CER) can be informed by synthesis of existing clinical information and analysis of observational data, randomized clinical trials (RCTs) are often needed to address real world evidence questions. However, traditionally designed RCTs are ill suited to address the IOM definition of CER: “comparison of effective interventions among patients in typical patient care settings, with decisions tailored to individual patient needs”. Bayesian and adaptive approaches can reduce sample size, time, and cost required to obtain decision-relevant information by incorporating existing high-quality external evidence (e.g. pivotal trials, systematic reviews, models, observational studies) into trial design and change or "adapt" in response to information generated during the trial allowing for focusing in on subpopulations who respond differentially. If new interventions become available, they can be added and less effective ones dropped without restarting the trial; therefore, at any given time, the trial is comparing the alternatives most relevant to current clinical practice. This dynamic "learning, adaptive" feature is analogous to the Institute of Medicine Evidence-Based Medicine Roundtable's "learning health care system". The session will provide a critical overview of these techniques and discuss the opportunities and challenges associated with their being applied in the evolving CER environment.