The papers included in this chapter offer observations on improvements needed in the design and interpretation of intervention trials; methods that take better advantage of system-level data; possible improvements in analytic tools, sample size, data quality, organization, and processing; and novel techniques that researchers are beginning to use in conjunction with new information, models, and tools.
Citing models from Duke University, The Society of Thoracic Surgeons (STS), and the Food and Drug Administration’s (FDA’s) Critical Path Clinical Trials Transformation Initiative, Robert M. Califf from Duke University discusses opportunities to improve the efficiency of clinical trials and to reduce their exorbitant costs. Innovations in the structure, strategy, conduct, analysis, and reporting of trials promise to make them less expensive, faster, more inclusive, and more responsive to important questions. Particular attention is needed to identify regulations that improve clinical trial quality and eliminate practices that increase costs without an equal return in value. Finally, establishing “envelopes of creativity” in which innovation is encouraged and supported is essential to maximizing the appropriate use of this methodology.
Confounding is often the biggest issue in effectiveness analyses of large databases. Innovative analytic tools are needed to make the best use of large clinical and administrative databases. Sebastian Schneeweiss from Harvard Medical School observes that instrumental variable analysis is an underused, but promising, approach for effectiveness analyses. Recent developments of note include approaches that exploit the concepts of proxy variables using high-dimensional propensity scores and provider variation in prescribing preference using instrumental variable analysis.
Rejecting any suggestion that “one trial = all trials,” Donald A. Berry from the University of Texas M.D. Anderson Cancer Center makes the case that adaptive and, particularly, Bayesian approaches lend themselves well to synthesizing and combining sources of information, such as meta-analyses, and provide means of modeling and assessing sources of uncertainty appropriately. Therefore, Berry asserts, they are ideally suited for experimental trial design.
Mark S. Roberts of the University of Pittsburgh, representing Archimedes Inc. at the workshop, suggests that physiology-based simulation and predictive models, such as an eponymous model developed at Archimedes, have the potential to augment and enhance knowledge gained from randomized controlled trials (RCTs) and can be used to fill “gaps” that are difficult or impractical to answer using clinical trial methods. Of particular relevance is the potential for these models to perform virtual comparative effectiveness trials.
This chapter concludes with a discussion of the dramatic expansion of information on genetic variation related to common, complex disease and