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A Bayesian Framework Allowing Incorporation of Retrospective Information in Prospective Diagnostic Biomarker-Validation Designs

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journal contribution
posted on 08.05.2019, 13:13 authored by Leandro García Barrado, Els Coart, Tomasz Burzykowski

The sample size of a prospective clinical study aimed at validation of a diagnostic biomarker-based test may be prohibitively large. We present a Bayesian framework that allows incorporating available development-study information about the performance of the test. As a result, the framework allows reducing the sample size required in the validation study, which may render the latter study feasible. The validation is based on the Bayesian testing of a hypothesis regarding possible values of AUC. Toward this end, first, available information is translated into a prior distribution. Next, this prior distribution is used in a Bayesian design to evaluate the performance of the diagnostic-test. We perform a simulation study to compare the power of the proposed Bayesian design to the approach ignoring development-study information. For each scenario, 1000 studies of sample size 100, 400, and 800 are simulated. Overall, the proposed Bayesian design leads to a substantially higher power than the flat-prior design. In some of the considered simulation settings, the Bayesian design requires as little as 50% of the flat-prior traditional design’s sample size to reach approximately the same power. Moreover, a simulation-based application strategy is proposed and presented with respect to a case-study involving the development of a biomarker-based diagnostic-test for Alzheimer’s disease.


This Research has been conducted with the financial support of the Walloon Government under the European Error! Hyperlink reference not valid. framework (Project B4AD, Agreement no. 1017106). It was conducted in collaboration with International Drug Development Institute (Louvain-la-Neuve, Belgium), PamGene International (Den Bosch, The Netherlands) and the VU University medical center and the Alzheimer center (Amsterdam, The Netherlands). The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Hercules Foundation and the Flemish Government—Department EWI. The support of the IAP Research Network of the Belgian state (Belgian Science Policy) P7/06 is acknowledged by the authors.