Literature DB >> 26412484

Sample size planning for phase II trials based on success probabilities for phase III.

Heiko Götte1, Armin Schüler1, Marietta Kirchner2, Meinhard Kieser2.   

Abstract

In recent years, high failure rates in phase III trials were observed. One of the main reasons is overoptimistic assumptions for the planning of phase III resulting from limited phase II information and/or unawareness of realistic success probabilities. We present an approach for planning a phase II trial in a time-to-event setting that considers the whole phase II/III clinical development programme. We derive stopping boundaries after phase II that minimise the number of events under side conditions for the conditional probabilities of correct go/no-go decision after phase II as well as the conditional success probabilities for phase III. In addition, we give general recommendations for the choice of phase II sample size. Our simulations show that unconditional probabilities of go/no-go decision as well as the unconditional success probabilities for phase III are influenced by the number of events observed in phase II. However, choosing more than 150 events in phase II seems not necessary as the impact on these probabilities then becomes quite small. We recommend considering aspects like the number of compounds in phase II and the resources available when determining the sample size. The lower the number of compounds and the lower the resources are for phase III, the higher the investment for phase II should be.
Copyright © 2015 John Wiley & Sons, Ltd.

Keywords:  drug development programme; phase II trials; probability of success; sample size; time-to-event analysis

Mesh:

Year:  2015        PMID: 26412484     DOI: 10.1002/pst.1717

Source DB:  PubMed          Journal:  Pharm Stat        ISSN: 1539-1604            Impact factor:   1.894


  1 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

  1 in total

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