gms | German Medical Science

64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

08. - 11.09.2019, Dortmund

Sample size calculation and reestimation of the prevalence in a confirmatory diagnostic accuracy study

Meeting Abstract

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  • Maria Stark - Department of Medical Biometry, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
  • Antonia Zapf - Department of Medical Biometry, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Dortmund, 08.-11.09.2019. Düsseldorf: German Medical Science GMS Publishing House; 2019. DocAbstr. 273

doi: 10.3205/19gmds007, urn:nbn:de:0183-19gmds0077

Veröffentlicht: 6. September 2019

© 2019 Stark et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe http://creativecommons.org/licenses/by/4.0/.


Gliederung

Text

Introduction: In a confirmatory diagnostic accuracy study, sensitivity and specificity are considered as co-primary endpoints. For the sample size calculation, the prevalence of the target population must be taken into account to receive a representative sample. In this context, a general problem arises as with a low or high prevalence, the study will be overpowered regarding one of the endpoints. One further issue is the correct specification of the true prevalence. With an incorrect assumption about the prevalence, an over- or underestimated sample size will result.

Methods: To prevent such an overpowered study, a method for an optimal sample size calculation for the comparison of a diagnostic index test with the reference standard is proposed. To face the problem of an incorrectly specified prevalence, a blinded one-time reestimation design of the prevalence is evaluated by a simulation study. This adaptive design is compared to a fixed design without a prevalence reestimation.

Results: The type I error rate of the blinded reestimation design is not inflated. The empirical overall power equals the desired theoretical power and the design offers unbiased estimates of the prevalence. The optimal size of the internal pilot study is 50% of the initially calculated sample size.

Conclusions: A one-time reestimation design of the prevalence based on the optimal sample size calculation is recommended in diagnostic accuracy studies.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.