Sample size reassessment in non-inferiority trials: internal pilot study designs with ANCOVA
Objectives: Analysis of covariance (ANCOVA) is widely applied in practice and its use is recommended by regulatory guidelines. However, the required sample size for ANCOVA depends on parameters that are usually uncertain in the planning phase of a study. Sample size recalculation within the internal...
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| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2011
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| In: |
Methods of information in medicine
Year: 2011, Jahrgang: 50, Heft: 3, Pages: 237-243 |
| ISSN: | 2511-705X |
| DOI: | 10.3414/ME09-01-0063 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.3414/ME09-01-0063 Verlag, lizenzpflichtig, Volltext: http://www.thieme-connect.de/DOI/DOI?10.3414/ME09-01-0063 
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| Verfasserangaben: | T. Friede, M. Kieser |
MARC
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| 520 | |a Objectives: Analysis of covariance (ANCOVA) is widely applied in practice and its use is recommended by regulatory guidelines. However, the required sample size for ANCOVA depends on parameters that are usually uncertain in the planning phase of a study. Sample size recalculation within the internal pilot study design allows to cope with this problem. From a regulatory viewpoint it is preferable that the treatment group allocation remains masked and that the type I error is controlled at the specified significance level. The characteristics of blinded sample size reassessment for ANCOVA in non-inferiority studies have not been investigated yet. We propose an appropriate method and evaluate its performance. Methods: In a simulation study, the characteristics of the proposed method with respect to type I error rate, power and sample size are investigated. It is illustrated by a clinical trial example how strict control of the significance level can be achieved. Results: A slight excess of the type I error rate beyond the nominal significance level was observed. The extent of exceedance increases with increasing non-inferiority margin and increasing correlation between outcome and covariate. The procedure assures the desired power over a wide range of scenarios even if nuisance parameters affecting the sample size are initially mis-specified. Conclusions: The proposed blinded sample size recalculation procedure protects from insufficient sample sizes due to incorrect assumptions about nuisance parameters in the planning phase. The original procedure may lead to an elevated type I error rate, but methods are available to control the nominal significance level. | ||
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