Artikel
Testing procedure, sample size determination and blinded sample size reestimation for testing treatment effects in nested subpopulations with adjustments for baseline covariates
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Autoren
Veröffentlicht: | 27. August 2018 |
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Gliederung
Text
The gold standard for the evaluation of efficacy and safety of new treatments are randomized controlled trials (RCT). Analyses of RCT are commonly adjusted for baseline covariates to minimize bias and increase the efficiency of the inference, i.e. higher power for hypothesis tests or shorter confidence intervals [1]. With an increasing interest in personalized treatment strategies more stratified trials investigating subgroups of patients are conducted. As in Placzek and Friede (2017) [2] we consider the analyses of continuous endpoints testing for treatment effects in several nested subgroups, which might be defined by thresholds of a biomarker. Here we extent their work by adjusting the analyses for baseline covariates considering an analysis of covariance (ANCOVA) model. Following Mehta et al. (2014) [3] we divide the population into disjunctive subsets, in which the hypotheses of interest are tested. Hence, the hypothesis tests for subpopulations consisting of one or more disjunctive subsets are tested by combining the p-values of those subsets. This approach was also considered by Graf et al. (2017) [4], however, without adjusting for baseline covariates. A method for sample size determination is presented. Furthermore, we propose a design with blinded sample size re-estimation correcting misspecifications of nuisance parameters in the sample size calculation. The performance of the proposed testing strategy, sample size determination and blinded reestimation are investigated using Monte Carlo simulations. The proposed methods are illustrated by a trial evaluating a fatigue intervention in multiple sclerosis [5].
The authors declare that they have no competing interests.
The authors declare that a positive ethics committee vote has been obtained.
References
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- European Medicines Agency. Points to consider on adjustment for baseline covariates. 2003. Available from: http://www.ema.europa.eu
- 2.
- Placzek M, Friede T. Clinical trials with nested subgroups: Analysis, sample size determination and internal pilot studies. Stat Methods Med Res. 2017 Jan;:962280217696116. DOI: 10.1177/0962280217696116
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- Graf AC, Wassmer G, Friede T, Gera RG, Posch M. Robustness of testing procedures for confirmatory subpopulation analyses based on a continuous biomarker. Stat Methods Med Res. 2018 Jan;:962280218777538. DOI: 10.1177/0962280218777538
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- Pöttgen J, Moss-Morris R, Wendebourg JM, Feddersen L, Lau S, Köpke S, Meyer B, Friede T, Penner IK, Heesen C, Gold SM. Randomised controlled trial of a self-guided online fatigue intervention in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2018 Mar;:. DOI: 10.1136/jnnp-2017-317463