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Summarising and validating test accuracy results across multiple studies for use in clinical practice.

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Abstract

Following a meta-analysis of test accuracy studies, the translation of summary results into clinical practice is potentially problematic. The sensitivity, specificity and positive (PPV) and negative (NPV) predictive values of a test may differ substantially from the average meta-analysis findings, because of heterogeneity. Clinicians thus need more guidance: given the meta-analysis, is a test likely to be useful in new populations, and if so, how should test results inform the probability of existing disease (for a diagnostic test) or future adverse outcome (for a prognostic test)? We propose ways to address this. Firstly, following a meta-analysis, we suggest deriving prediction intervals and probability statements about the potential accuracy of a test in a new population. Secondly, we suggest strategies on how clinicians should derive post-test probabilities (PPV and NPV) in a new population based on existing meta-analysis results and propose a cross-validation approach for examining and comparing their calibration performance. Application is made to two clinical examples. In the first example, the joint probability that both sensitivity and specificity will be >80% in a new population is just 0.19, because of a low sensitivity. However, the summary PPV of 0.97 is high and calibrates well in new populations, with a probability of 0.78 that the true PPV will be at least 0.95. In the second example, post-test probabilities calibrate better when tailored to the prevalence in the new population, with cross-validation revealing a probability of 0.97 that the observed NPV will be within 10% of the predicted NPV.

Citation

(2015). Summarising and validating test accuracy results across multiple studies for use in clinical practice. Statistics in Medicine, 2081 - 2103. https://doi.org/10.1002/sim.6471

Acceptance Date Feb 17, 2015
Publication Date Jun 15, 2015
Journal Stat Med
Print ISSN 0277-6715
Publisher Wiley
Pages 2081 - 2103
DOI https://doi.org/10.1002/sim.6471
Publisher URL http://onlinelibrary.wiley.com/doi/10.1002/sim.6471/abstract

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