title: Tradeoff-based optimization criteria in clinical trials with multiple objectives and adaptive designs.
abstract: The article discusses clinical trial optimization problems in the context of mid- to late-stage drug development. Using the Clinical Scenario Evaluation approach, main objectives of clinical trial optimization are formulated, including selection of clinically relevant optimization criteria, identification of sets of optimal and nearly optimal values of the parameters of interest, and sensitivity assessments. The paper focuses on a class of optimization criteria arising in clinical trials with several competing goals, termed tradeoff-based optimization criteria, and discusses key considerations in constructing and applying tradeoff-based criteria. The clinical trial optimization framework considered in the paper is illustrated using two case studies based on a clinical trial with multiple objectives and a two-stage clinical trial which utilizes adaptive decision rules.
authors.affiliation: a Center for Statistics in Drug Development, Quintiles , Overland Park , Kansas , USA.
authors.forename: Alex
authors.initials: A
authors.lastname: Dmitrienko
authors.affiliation: b Oncology Biostatistics, Institut de Recherches Internationales Servier (I.R.I.S.) , Suresnes , France.
authors.forename: Gautier
authors.initials: G
authors.lastname: Paux
authors.affiliation: c Biostatistics, MedImmune , Gaithersburg , Maryland , USA.
authors.forename: Erik
authors.initials: E
authors.lastname: Pulkstenis
authors.affiliation: c Biostatistics, MedImmune , Gaithersburg , Maryland , USA.
authors.forename: Jianliang
authors.initials: J
authors.lastname: Zhang
mesh_terms: D002986:Clinical Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D006040:Goals; D006801:Humans; D012107:Research Design / Q000706:statistics & numerical data*; D018401:Sample Size
keywords: Adaptive designs; clinical scenario evaluation; clinical trial optimization; clinical trials; multiple testing