Test-compatible confidence intervals for adaptive two-stage single-arm designs with binary endpoint
Inference after two-stage single-arm designs with binary endpoint is challenging due to the nonunique ordering of the sampling space in multistage designs. We illustrate the problem of specifying test-compatible confidence intervals for designs with nonconstant second-stage sample size and present t...
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| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2018
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| In: |
Biometrical journal
Year: 2018, Jahrgang: 60, Heft: 1, Pages: 196-206 |
| ISSN: | 1521-4036 |
| DOI: | 10.1002/bimj.201700018 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1002/bimj.201700018 Verlag, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/bimj.201700018 
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| Verfasserangaben: | Kevin Kunzmann, Meinhard Kieser |
| Zusammenfassung: | Inference after two-stage single-arm designs with binary endpoint is challenging due to the nonunique ordering of the sampling space in multistage designs. We illustrate the problem of specifying test-compatible confidence intervals for designs with nonconstant second-stage sample size and present two approaches that guarantee confidence intervals consistent with the test decision. Firstly, we extend the well-known Clopper-Pearson approach of inverting a family of two-sided hypothesis tests from the group-sequential case to designs with fully adaptive sample size. Test compatibility is achieved by using a sample space ordering that is derived from a test-compatible estimator. The resulting confidence intervals tend to be conservative but assure the nominal coverage probability. In order to assess the possibility of further improving these confidence intervals, we pursue a direct optimization approach minimizing the mean width of the confidence intervals. While the latter approach produces more stable coverage probabilities, it is also slightly anti-conservative and yields only negligible improvements in mean width. We conclude that the Clopper-Pearson-type confidence intervals based on a test-compatible estimator are the best choice if the nominal coverage probability is not to be undershot and compatibility of test decision and confidence interval is to be preserved. |
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| Beschreibung: | First published: 27 October 2017 Gesehen am 05.11.2018 |
| Beschreibung: | Online Resource |
| ISSN: | 1521-4036 |
| DOI: | 10.1002/bimj.201700018 |