Blinded versus unblinded covariate selection in confirmatory survival Trials
Adjustment for covariates and specification of the correct covariate set are important issues in the analysis of clinical trials. Edwards (1999) proposes a model selection approach where the model is chosen on the final data set, which remains blinded for treatment group allocation. We investigate t...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
07 Mar 2014
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| In: |
Journal of biopharmaceutical statistics
Year: 2014, Jahrgang: 24, Heft: 2, Pages: 398-414 |
| ISSN: | 1520-5711 |
| DOI: | 10.1080/10543406.2013.860158 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1080/10543406.2013.860158
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| Verfasserangaben: | Christina Kunz and Meinhard Kieser |
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| 520 | |a Adjustment for covariates and specification of the correct covariate set are important issues in the analysis of clinical trials. Edwards (1999) proposes a model selection approach where the model is chosen on the final data set, which remains blinded for treatment group allocation. We investigate this method for time-to-event endpoints and compare its performance to variable selection within an adaptive design. This adaptive design integrates the methods of Schäfer and Müller (2001) and Keiding et al. (1987) and allows variable selection on the unblinded data during an interim analysis. Monte Carlo simulation shows that Edwards’ method—though blinded—outperforms the adaptive method in terms of ability to select the survival relevant covariates and power. The application of the methods is illustrated by a clinical trial example. | ||
| 650 | 4 | |a Adaptive design | |
| 650 | 4 | |a Blinded data | |
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| 650 | 4 | |a Covariate selection | |
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| 650 | 4 | |a Probability principle | |
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