¹Institut für Medizinische Biometrie und Epidemiologie, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
²Charité - Universitätsmedizin Berlin, Berlin, Germany

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Introduction: The accuracy of diagnostic tests is commonly evaluated by estimating the area under the receiver operating characteristic curve (AUC), as well as sensitivity and specificity at given diagnostic cut-offs.

Challenges: However, many diagnostic trials use factorial designs. For example, different combinations of readers and methods may be used to diagnose a patient. Furthermore, diagnostic studies may generate clustered data by repeated measurements over time or several lesions, for example different brain regions. Dependencies between a person's observations must be taken into account in the analysis in order to prevent variance deflation. Lange [1] developed a nonparametric mathematical framework to deal with both of these design aspects, and Lange and Brunner generalized the approach from the AUC to sensitivity and specificity [2].

Additionally, it may be of interest to adjust the estimation procedure of the above mentioned accuracy measures for covariates. For example, it may be the case that age, weight or height influence the diagnostic accuracy of a test. Zapf [3] proposed a nonparametric methodological approach to adjust the AUC for such covariates, while also allowing for factorial designs, but not yet for clustered data.

Conclusion: In this talk we present a new, unified method that enables covariate adjustment of the AUC, sensitivity and specificity in studies with factorial designs and clustered data. We will show the properties of the approach using simulated data and illustrate the approach with an example study.

The authors declare that they have no competing interests.

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

The contribution has already been published: [4]

Literatur

[1] Lange K. Nichtparametrische analyse diagnostischer Gütemaße bei Clusterdaten [Dissertation]. 2011. DOI: 10.53846/goediss-3538
[2] Lange K, Brunner E. Sensitivity, specificity and ROC-curves in multiple reader diagnostic trials—a unified, nonparametric approach. Statistical Methodology. 2012;9(4):490–500. DOI: 10.1016/j.stamet.2011.12.002
[3] Zapf A. Multivariates nichtparametrisches Behrens-Fisher-problem MIT Kovariablen [Dissertation]. 2009. DOI: 10.53846/goediss-2488
[4] Weber P, Kramer K, Zapf A. Covariate adjustment, factorial designs and clustered data in diagnostic accuracy studies. In: 70th Biometric Colloquium; 2024 Feb 28 – Mar 01; Lübeck, Germany.