Probability of success for phase III after exploratory biomarker analysis in phase II
The probability of success or average power describes the potential of a future trial by weighting the power with a probability distribution of the treatment effect. The treatment effect estimate from a previous trial can be used to define such a distribution. During the development of targeted ther...
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| Main Authors: | , , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
23 February 2017
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| In: |
Pharmaceutical statistics
Year: 2017, Volume: 16, Issue: 3, Pages: 178-191 |
| ISSN: | 1539-1612 |
| DOI: | 10.1002/pst.1804 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1002/pst.1804 Verlag, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/pst.1804 
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| Author Notes: | Heiko Götte, Marietta Kirchner, Martin Oliver Sailer |
MARC
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| 520 | |a The probability of success or average power describes the potential of a future trial by weighting the power with a probability distribution of the treatment effect. The treatment effect estimate from a previous trial can be used to define such a distribution. During the development of targeted therapies, it is common practice to look for predictive biomarkers. The consequence is that the trial population for phase III is often selected on the basis of the most extreme result from phase II biomarker subgroup analyses. In such a case, there is a tendency to overestimate the treatment effect. We investigate whether the overestimation of the treatment effect estimate from phase II is transformed into a positive bias for the probability of success for phase III. We simulate a phase II/III development program for targeted therapies. This simulation allows to investigate selection probabilities and allows to compare the estimated with the true probability of success. We consider the estimated probability of success with and without subgroup selection. Depending on the true treatment effects, there is a negative bias without selection because of the weighting by the phase II distribution. In comparison, selection increases the estimated probability of success. Thus, selection does not lead to a bias in probability of success if underestimation due to the phase II distribution and overestimation due to selection cancel each other out. We recommend to perform similar simulations in practice to get the necessary information about the risk and chances associated with such subgroup selection designs. | ||
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