title: Some challenges with statistical inference in adaptive designs.
abstract: Adaptive designs have generated a great deal of attention to clinical trial communities. The literature contains many statistical methods to deal with added statistical uncertainties concerning the adaptations. Increasingly encountered in regulatory applications are adaptive statistical information designs that allow modification of sample size or related statistical information and adaptive selection designs that allow selection of doses or patient populations during the course of a clinical trial. For adaptive statistical information designs, a few statistical testing methods are mathematically equivalent, as a number of articles have stipulated, but arguably there are large differences in their practical ramifications. We pinpoint some undesirable features of these methods in this work. For adaptive selection designs, the selection based on biomarker data for testing the correlated clinical endpoints may increase statistical uncertainty in terms of type I error probability, and most importantly the increased statistical uncertainty may be impossible to assess.
authors.affiliation: a Division of Biometrics I , Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, Food and Drug Administration , Silver Spring , Maryland , USA.
authors.forename: H M James
authors.initials: HM
authors.lastname: Hung
authors.forename: Sue-Jane
authors.initials: SJ
authors.lastname: Wang
authors.forename: Peiling
authors.initials: P
authors.lastname: Yang
mesh_terms: D002986:Clinical Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D015588:Observer Variation; D012107:Research Design; D018401:Sample Size; D016896:Treatment Outcome
keywords: Adaptive selection; Adaptive statistical information; Biomarker; Marker; Unweighted Z statistic; Weighted Z statistic