Artikel
Sample size re-estimation based on the prevalence in a randomized test-treatment study
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Veröffentlicht: | 24. September 2021 |
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Gliederung
Text
Patient benefit should be the primary criterion in evaluating diagnostic tests. If a new test has shown sufficient accuracy, its application in clinical practice should yield to a patient benefit. Randomized test-treatment studies are needed to assess the clinical utility of a diagnostic test as part of a broader management regimen in which test-treatment strategies are compared in terms of their impact on patient relevant outcomes [1]. Due to their increased complexity compared to common intervention trials the implementation of such studies poses practical challenges which might affect the validity of the study. One important aspect is the sample size determination. It is a special feature of these designs that they combine information on the disease prevalence and accuracy of the diagnostic tests, i.e. sensitivity and specificity of the investigated tests, with assumptions on the expected treatment effect. Due to the lack of empirical information or uncertainty regarding these parameters sample size consideration will always be based on a rather weak foundation, thus leading to an over- or underpowered trial. Therefore, it is reasonable to consider adaptations in earlier phases of the trial based on a pre-specified interim analysis in order to solve this problem. A blinded sample size re-estimation based on the disease prevalence in a randomized test-treatment study was performed as part of a simulation study. The type I error, the empirical overall power as well as the bias of the estimated prevalence are assessed and presented.
The authors declare that they have no competing interests.
The authors declare that an ethics committee vote is not required.
This contribution has already been published: Biometrisches Kolloquium 2021
References
- 1.
- Lijmer JG, Bossuyt PM. Diagnostic testing and prognosis: the randomized controlled trial in test evaluation research. In: The evidence base of clinical diagnosis. Blackwell Oxford; 2009. p. 63-82.