Evidence map›Paper›PMID 31890896›Full record

ArticleDiagnostic and prognostic research2019

Targeted test evaluation: a framework for designing diagnostic accuracy studies with clear study hypotheses.

Daniël A Korevaar, Gowri Gopalakrishna, Jérémie F Cohen, Patrick M Bossuyt

Abstract read
In one paragraph

Article in Diagnostic and prognostic research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  14. Snake Fungal Disease (Ophidiomycosis) in Northern Pine Snakes (Journal of fungi (Basel, Switzerland) · 2025
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  17. Snake Fungal Disease in Free-Ranging Northern Pine Snakes (Journal of fungi (Basel, Switzerland) · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Daniël A Korevaar1Department of Respiratory Medicine, Academic Medical Center, Amsterdam University Medical Centers, Amsterdam, the Netherlands.ORCID 0000-0002-7979-7897
Gowri Gopalakrishna2Department of Epidemiology and Biostatistics, Vrije University Medical Centre, Amsterdam University Medical Centers, Amsterdam, the Netherlands.
Jérémie F Cohen3Department of General Pediatrics and Pediatric Infectious Diseases, Necker-Enfants Malades Hospital, APHP, Paris Descartes University, Paris, France.
Patrick M Bossuyt5Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, Amsterdam University Medical Centers, Amsterdam, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Most randomized controlled trials evaluating medical interventions have a pre-specified hypothesis, which is statistically tested against the null hypothesis of no effect. In diagnostic accuracy studies, study hypotheses are rarely pre-defined and sample size calculations are usually not performed, which may jeopardize scientific rigor and can lead to over-interpretation or "spin" of study findings. In this paper, we propose a strategy for defining meaningful hypotheses in diagnostic accuracy studies. Based on the role of the index test in the clinical pathway and the downstream consequences of test results, the consequences of test misclassifications can be weighed, to arrive at minimally acceptable criteria for pre-defined test performance: levels of sensitivity and specificity that would justify the test's intended use. Minimally acceptable criteria for test performance should form the basis for hypothesis formulation and sample size calculations in diagnostic accuracy studies.

Identifiers

PMID31890896
PMCPMC6921417

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.