title: Adaptive designs: looking for a needle in the haystack-a new challenge in medical research.
abstract: The statistical principles of fully adaptive designs are outlined. The options of flexibility and the price to be paid in terms of statistical properties of the test procedures are discussed. It is stressed that controlled inference after major design modifications (changing hypotheses) will include a penalty: Intersections among all the hypotheses considered throughout the trial have to be rejected before testing individual hypotheses. Moreover, feasibility in terms of integrity and persuasiveness of the results achieved after adaptations based on unblinded data is considered as the crucial issue in practice. In the second part, sample size adaptive procedures are considered testing a large number of hypotheses under constraints on total sample size as in genetic studies. The advantage of sequential procedures is sketched for the example of two-stage designs with a pilot phase for screening promising hypotheses (markers) and controlling the false discovery rate. Finally, we turn to the clinical problem how to select markers and estimate a score from limited samples, e.g. for predicting the response to therapy of a future patient. The predictive ability of such scores will be rather poor when investigating a large number of hypotheses and truly large marker effects are lacking. An obvious dilemma will show up: More optimistic selection rules may be superior if in fact effective markers exist, but will produce more nuisance prediction if no effective markers exist compared with more cautious strategies, e.g. aiming at some control of type I error probabilities.
authors.affiliation: Section of Medical Statistics, Medical University of Vienna, Vienna, Austria. peter.bauer@meduniwien.ac.at
authors.forename: Peter
authors.initials: P
authors.lastname: Bauer
mesh_terms: D035843:Biomedical Research / Q000379:methods* / Q000706:statistics & numerical data; D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic / Q000379:methods; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D012372:ROC Curve; D012107:Research Design