Flexible Designs by Adaptive Plans of Generalized Pocock- and O'Brien-Fleming-Type and by Self-Designing Clinical Trials

• Published in: Biometrical journal 2006-08, Vol.48 (4), p.521-536
• Main Author: Hartung, Joachim
• Format: Article
• Language: English
• Subjects: Adaptive planning; Applications; Biology, psychology, social sciences; Clinical Trials as Topic - statistics & numerical data; Data Interpretation, Statistical; Exact sciences and technology; Experimental design; Flexible designs; Generalized inverse chi-square method; Group sequential clinical trials; Mathematics; Models, Statistical; O'Brien-Fleming-design; Pocock-design; Probability and statistics; Research Design - statistics & numerical data; Sample Size; Sciences and techniques of general use; Self-designing; Statistics; Weighted inverse normal method
• ISSN: 0323-3847; 1521-4036
• DOI: 10.1002/bimj.200510212

Flexible designs are provided by adaptive planning of sample sizes as well as by introducing the weighted inverse normal combining method and the generalized inverse chi‐square combining method in the context of conducting trials consecutively step by step. These general combining methods allow quite different weighting of sequential study parts, also in a completely adaptive way, based on full information from unblinded data in previously performed stages. So, in reviewing some basic developments of flexible designing, we consider a generalizing approach to group sequentially performed clinical trials of Pocock‐type, of O'Brien‐Fleming‐type, and of Self‐designing‐type. A clinical trial may be originally planned either to show non‐inferiority or superiority. The proposed flexible designs, however, allow in each interim analysis to change the planning from showing non‐inferiority to showing superiority and vice versa. Several examples of clinical trials with normal and binary outcomes are worked out in detail. We demonstrate the practicable performance of the discussed approaches, confirmed in an extensive simulation study. Our flexible designing is a useful tool, provided that a priori information about parameters involved in the trial is not available or subject to uncertainty. (© 2006 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)