title: Adaptive clinical trials in oncology.
abstract: Modern oncology drug development faces challenges very different from those of the past and it must adapt accordingly. The size and expense of phase III clinical trials continue to increase, but the success rate remains unacceptably low. Adaptive trial designs can make development more informative, addressing whether a drug is safe and effective while showing how it should be delivered and to whom. An adaptive design is one in which the accumulating data are used to modify the trial's course. Adaptive designs are ideal for addressing many questions at once. For example, a single trial might identify the appropriate patient population, dose and regimen, and therapeutic combinations, and then switch seamlessly into a phase III confirmatory trial. Adaptive designs rely on information, including from patients who have not achieved the trial's primary end point. Longitudinal models of biomarkers (including tumor burden assessed via imaging) enable predictions of primary end points. Taking a Bayesian perspective facilitates building an efficient and accurate trial, including using longitudinal information. A wholly new paradigm for drug development exemplifying personalized medicine is evinced by an adaptive trial called I-SPY2, in which drugs from many companies are evaluated in the same trial--a phase II screening process.
authors: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, 4-5062 Pickens Academic Tower, Houston, TX 77030-1402, USA. dberry@mdanderson.org
authors: Donald A
authors: DA
authors: Berry
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D055808:Drug Discovery / Q000706:statistics & numerical data*; D006801:Humans; D008137:Longitudinal Studies; D057285:Precision Medicine; D012107:Research Design / Q000706:statistics & numerical data*