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. 2019 Sep 3;8(1):226.
doi: 10.1186/s13643-019-1131-4.

An algorithm for the classification of study designs to assess diagnostic, prognostic and predictive test accuracy in systematic reviews

Tim Mathes  1 , Dawid Pieper  2
Affiliations

An algorithm for the classification of study designs to assess diagnostic, prognostic and predictive test accuracy in systematic reviews

Tim Mathes et al. Syst Rev. .

Abstract

Results of medical tests are the main source to inform clinical decision making. The main information to assess the usefulness of medical tests for correct discrimination of patients are accuracy measures. For the estimation of test accuracy measures, many different study designs can be used. The study design is related to the clinical question to be answered (diagnosis, prognosis, prediction), determines the accuracy measures that can be calculated and it might have an influence on risk of bias. Therefore, a clear and consistent distinction of the different study designs in systematic reviews on test accuracy studies is very important. In this paper, we propose an algorithm for the classification of study designs of test accuracy, that compare the results of an index test (the test to be evaluated) with the results of a reference test (the test whose results are considered as correct/the gold standard) studies in systematic reviews.

Keywords: Diagnosis prognosis; Diagnostic accuracy; Prediction; Sensitivity; Specificity; Study design classification.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Algorithm for classification of test accuracy study desings
See this image and copyright information in PMC

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