Literature DB >> 26256550

Utility-based optimization of phase II/III programs.

Marietta Kirchner1, Meinhard Kieser1, Heiko Götte2, Armin Schüler2.   

Abstract

Phase II and phase III trials play a crucial role in drug development programs. They are costly and time consuming and, because of high failure rates in late development stages, at the same time risky investments. Commonly, sample size calculation of phase III is based on the treatment effect observed in phase II. Therefore, planning of phases II and III can be linked. The performance of the phase II/III program crucially depends on the allocation of the resources to phases II and III by appropriate choice of the sample size and the rule applied to decide whether to stop the program after phase II or to proceed. We present methods for a program-wise phase II/III planning that aim at determining optimal phase II sample sizes and go/no-go decisions in a time-to-event setting. Optimization is based on a utility function that takes into account (fixed and variable) costs of the drug development program and potential gains after successful launch. The proposed methods are illustrated by application to a variety of scenarios typically met in oncology drug development.
Copyright © 2015 John Wiley & Sons, Ltd.

Keywords:  drug development; expected utility; optimization; probability of success; program-wise planning

Mesh:

Year:  2015        PMID: 26256550     DOI: 10.1002/sim.6624

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  2 in total

1.  Optimal designs for phase II/III drug development programs including methods for discounting of phase II results.

Authors:  Stella Erdmann; Marietta Kirchner; Heiko Götte; Meinhard Kieser
Journal:  BMC Med Res Methodol       Date:  2020-10-09       Impact factor: 4.615

2.  Optimizing Trial Designs for Targeted Therapies.

Authors:  Thomas Ondra; Sebastian Jobjörnsson; Robert A Beckman; Carl-Fredrik Burman; Franz König; Nigel Stallard; Martin Posch
Journal:  PLoS One       Date:  2016-09-29       Impact factor: 3.240

  2 in total

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