Optimal sample size allocation and go/no-go decision rules for phase II/III programs where several phase III trials are performed
The conduct of phase II and III programs is costly, time-consuming and, due to high failure rates in late development stages, risky. There is a strong connection between phase II and III trials as the go/no-go decision and the sample size chosen for phase III are based on the results observed in pha...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2019
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| In: |
Biometrical journal
Year: 2019, Jahrgang: 61, Heft: 2, Pages: 357-378 |
| ISSN: | 1521-4036 |
| DOI: | 10.1002/bimj.201700241 |
| Online-Zugang: | Verlag, Volltext: https://doi.org/10.1002/bimj.201700241 Verlag, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.201700241 
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| Verfasserangaben: | Stella Preussler, Meinhard Kieser, Marietta Kirchner |
MARC
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| 245 | 1 | 0 | |a Optimal sample size allocation and go/no-go decision rules for phase II/III programs where several phase III trials are performed |c Stella Preussler, Meinhard Kieser, Marietta Kirchner |
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| 520 | |a The conduct of phase II and III programs is costly, time-consuming and, due to high failure rates in late development stages, risky. There is a strong connection between phase II and III trials as the go/no-go decision and the sample size chosen for phase III are based on the results observed in phase II. An integrated planning of phase II and III is therefore reasonable. The success of phase II/III programs crucially depends on the allocation of the resources to phase II and III in terms of sample size and the rule applied to decide whether to stop or to proceed with phase III. Recently, a utility-based approach was proposed, where optimal planning of phase II/III programs is achieved by taking fixed and variable costs of the drug development program and potential gains after a successful launch into account. However, this method is restricted to programs with a single phase III trial, while regulatory authorities usually require statistical significance in two or more phase III trials. We present a generalization of this procedure to programs where two or more phase III trials are performed. Optimal phase II sample sizes and go/no-go decision rules are provided for time-to-event outcomes and cases, where at least one, two, or three phase III trials need to be successful. Different drug development program strategies (e.g. one large vs. two phase III trials) are compared within these different cases. Application to practical examples typically met in oncology trials illustrates the proposed method. | ||
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