Two-Stage Adaptive Designs With Interim Treatment Selection
|Other Titles:||Zweistufige Adaptive Designs mit Zwischenbehandlungsauswahl||Authors:||Carreras, Maximo Ariel||Supervisor:||Brannath, Werner||1. Expert:||Bretz, Frank||2. Expert:||Gutjahr, Georg||Abstract:||
This dissertation is about two-stage adaptive designs with interim treatment selection. It includes two articles entitled (1) Shrinkage estimation in two-stage adaptive designs with midtrial treatment selection and (2) Adaptive seamless designs with interim treatment selection: a case study in oncology. Both articles are published in the journal Statistics in Medicine. Adaptive designs for clinical trials allow interim data-driven design modifications while maintaining the rigor and validity of the statistical inference . Design adaptations may include early stopping for efficacy, futility or safety, reassessment of the overall sample size, adjustments to the study population (e.g. restriction to a sub-population), changes to endpoints or hypothesis to be tested as well as dropping or adding treatment arms. Adaptive designs have gained considerable popularity in recent years among clinical trialists because of their potential to improve efficiency in drug development. There are also ethical considerations that support the use of adaptive designs; for example, adaptive designs can reduce the number of patients within the trial who are treated with non-effective treatments. Overall, adaptive designs provide the same scientific rigor that is required in more traditional study designs while potentially utilizing fewer resources. However, while the increase in flexibility of adaptive designs offers great opportunities, it also brings limitations and pitfalls which should be carefully assessed when adaptive designs are intended to be used in confirmatory trials , . In the traditional drug development process, a phase II study typically compares several treatments (e.g. different doses of a new compound) to a control. The objective of the study is to determine whether the development of the compound should be continued and, if so, which treatment(s) or dose(s) should be further investigated. The phase II study also provides initial estimates of treatment effect, which are used to power the subsequent phase III study. The phase III study is then conducted as a stand-alone confirmatory study, disregarding all data collected on the phase II study. One of the most appealing applications of adaptive designs is to combine phase II and phase III studies of the traditional drug development process into a single seamless phase II/III confirmatory study (see  and  for comprehensive summaries). Bauer and Kieser  proposed two-stage adaptive designs that allow the integration of the treatment selection and the confirmatory testing of efficacy for the selected treatments within a single study. An important feature of these designs is the fact that the treatment selection rule does not need to be pre-specified, which gives considerable flexibility to the inherently complex interim decision process. Hommel  extended Bauer and Kieser's work to allow design modifications which include interim changes to the primary endpoint as well as addition of experimental treatments. All these adaptive designs (see also ) propose tests that control the family-wise type I error rate in the strong sense; that is, the probability that any treatment is erroneously declared significantly superior to the control is maintained below a pre-specified significance level under all possible configurations of effective and ineffective treatments. In the introduction of the dissertation, we discuss hypothesis testing and estimation in two-stage adaptive designs with interim treatment selection, which serve as a summary of the topics developed in the two articles.
|Keywords:||Best linear unbiased predictor (BLUP); Bayes risk; clinical trial; empirical Bayes; Lindley s estimator; random effects model; seamless phase II/III design; selection bias; selection mean squared error; adaptive seamless designs; treatment selection; surrogate endpoints; family wise error rate; confidence intervals; selection bias||Issue Date:||30-Apr-2015||URN:||urn:nbn:de:gbv:46-00104453-13||Institution:||Universität Bremen||Faculty:||FB3 Mathematik/Informatik|
|Appears in Collections:||Dissertationen|
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