Evidence map›Paper›PMID 25361619›Full record

SynthesisPsychometrika2015

Meta-analysis of Diagnostic Accuracy and ROC Curves with Covariate Adjusted Semiparametric Mixtures.

Philipp Doebler, Heinz Holling

Abstract readMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Psychometrika, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 49 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
75citing papers in PubMed, 49 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

75 citing papers in PubMed, 49 syntheses or guidelines pooled it.

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15 more citing papers are in PubMed but not listed here.

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

2 authors.

Philipp DoeblerInstitut für Psychologie, Westfälische Wilhelms-Universität, Fliednerstr. 21, 48149 , Münster, Germany. doebler@uni-muenster.de.
Heinz HollingInstitut für Psychologie, Westfälische Wilhelms-Universität, Fliednerstr. 21, 48149 , Münster, Germany. holling@uni-muenster.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many screening tests dichotomize a measurement to classify subjects. Typically a cut-off value is chosen in a way that allows identification of an acceptable number of cases relative to a reference procedure, but does not produce too many false positives at the same time. Thus for the same sample many pairs of sensitivities and false positive rates result as the cut-off is varied. The curve of these points is called the receiver operating characteristic (ROC) curve. One goal of diagnostic meta-analysis is to integrate ROC curves and arrive at a summary ROC (SROC) curve. Holling, Böhning, and Böhning (Psychometrika 77:106-126, 2012a) demonstrated that finite semiparametric mixtures can describe the heterogeneity in a sample of Lehmann ROC curves well; this approach leads to clusters of SROC curves of a particular shape. We extend this work with the help of the [Formula: see text] transformation, a flexible family of transformations for proportions. A collection of SROC curves is constructed that approximately contains the Lehmann family but in addition allows the modeling of shapes beyond the Lehmann ROC curves. We introduce two rationales for determining the shape from the data. Using the fact that each curve corresponds to a natural univariate measure of diagnostic accuracy, we show how covariate adjusted mixtures lead to a meta-regression on SROC curves. Three worked examples illustrate the method.

Indexed as

ROC CurveAlgorithmsHumansPsychometricsSurveys and Questionnairesdiagnostic meta-analysismeta-regressionsemiparametric mixturessummary receiver operating characteristic curvetransformations

Identifiers

PMID25361619

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.