Simulation-based adjustment after exploratory biomarker subgroup selection in phase II
As part of the evaluation of phase II trials, it is common practice to perform exploratory subgroup analyses with the aim of identifying patient populations with a beneficial treatment effect. When investigating targeted therapies, these subgroups are typically defined by biomarkers. Promising resul...
Gespeichert in:
| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
24 April 2017
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| In: |
Statistics in medicine
Year: 2017, Jahrgang: 36, Heft: 15, Pages: 2378-2390 |
| ISSN: | 1097-0258 |
| DOI: | 10.1002/sim.7294 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1002/sim.7294 Verlag, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7294 
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| Verfasserangaben: | Heiko Götte, Marietta Kirchner, Martin Oliver Sailer, Meinhard Kieser |
MARC
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| 520 | |a As part of the evaluation of phase II trials, it is common practice to perform exploratory subgroup analyses with the aim of identifying patient populations with a beneficial treatment effect. When investigating targeted therapies, these subgroups are typically defined by biomarkers. Promising results may lead to the decision to select the respective subgroup as the target population for a subsequent phase III trial. However, a selection based on a large observed treatment effect may potentially induce an upwards-bias leading to over-optimistic expectations on the success probability of the phase III trial. We describe how Approximate Bayesian Computation techniques can be used to derive a simulation-based bias adjustment method in this situation. Recommendations for the implementation of the approach are given. Simulation studies show that the proposed method reduces bias substantially compared with the maximum likelihood estimator. The procedure is illustrated with data from an oncology trial. Copyright © 2017 John Wiley & Sons, Ltd. | ||
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