Optimal planning of phase II/III programs for clinical trials with multiple endpoints
Owing to increased costs and competition pressure, drug development becomes more and more challenging. Therefore, there is a strong need for improving efficiency of clinical research by developing and applying methods for quantitative decision making. In this context, the integrated planning for pha...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
26 April 2018
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| In: |
Pharmaceutical statistics
Year: 2018, Jahrgang: 17, Heft: 5, Pages: 437-457 |
| ISSN: | 1539-1612 |
| DOI: | 10.1002/pst.1861 |
| Online-Zugang: | Verlag, Volltext: https://doi.org/10.1002/pst.1861 Verlag, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1861 
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| Verfasserangaben: | Meinhard Kieser, Marietta Kirchner, Eva Dölger, Heiko Götte |
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| 520 | |a Owing to increased costs and competition pressure, drug development becomes more and more challenging. Therefore, there is a strong need for improving efficiency of clinical research by developing and applying methods for quantitative decision making. In this context, the integrated planning for phase II/III programs plays an important role as numerous quantities can be varied that are crucial for cost, benefit, and program success. Recently, a utility-based framework has been proposed for an optimal planning of phase II/III programs that puts the choice of decision boundaries and phase II sample sizes on a quantitative basis. However, this method is restricted to studies with a single time-to-event endpoint. We generalize this procedure to the setting of clinical trials with multiple endpoints and (asymptotically) normally distributed test statistics. Optimal phase II sample sizes and go/no-go decision rules are provided for both the “all-or-none” and “at-least-one” win criteria. Application of the proposed method is illustrated by drug development programs in the fields of Alzheimer disease and oncology. | ||
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