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. 2022 Jul 25;22(1):205.
doi: 10.1186/s12874-022-01678-7.

Sample size recalculation based on the prevalence in a randomized test-treatment study

Amra Hot  1 , Norbert Benda  2 , Patrick M Bossuyt  3 , Oke Gerke  4   5 , Werner Vach  6   7 , Antonia Zapf  8
Affiliations

Sample size recalculation based on the prevalence in a randomized test-treatment study

Amra Hot et al. BMC Med Res Methodol. .

Abstract

Background: Randomized test-treatment studies aim to evaluate the clinical utility of diagnostic tests by providing evidence on their impact on patient health. However, the sample size calculation is affected by several factors involved in the test-treatment pathway, including the prevalence of the disease. Sample size planning is exposed to strong uncertainties in terms of the necessary assumptions, which have to be compensated for accordingly by adjusting prospectively determined study parameters during the course of the study.

Method: An adaptive design with a blinded sample size recalculation in a randomized test-treatment study based on the prevalence is proposed and evaluated by a simulation study. The results of the adaptive design are compared to those of the fixed design.

Results: The adaptive design achieves the desired theoretical power, under the assumption that all other nuisance parameters have been specified correctly, while wrong assumptions regarding the prevalence may lead to an over- or underpowered study in the fixed design. The empirical type I error rate is sufficiently controlled in the adaptive design as well as in the fixed design.

Conclusion: The consideration of a blinded recalculation of the sample size already during the planning of the study may be advisable in order to increase the possibility of success as well as an enhanced process of the study. However, the application of the method is subject to a number of limitations associated with the study design in terms of feasibility, sample sizes needed to be achieved, and fulfillment of necessary prerequisites.

Keywords: Adaptive design; Prevalence; Sample size recalculation; Sensitivity; Specificity.

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Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Fig. 1
Fig. 1
A schematic representation of a classical randomized test-treatment study [4, 5]
Fig. 2
Fig. 2
Results for the power for the 1620 scenarios stratified by the difference in prevalence, the difference in sensitivity, and the difference in specificity. The power of the fixed design was compared to the adaptive design containing a re-estimation of the prevalence assuming μI+ = 0.05 (a-c) and μI+ = 0.1 (d-f). The black dotted line mark the theoretical power of 80%. The black bold line in the box marks the median value
See this image and copyright information in PMC

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