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Meta-analysis and meta-modelling for diagnostic problems

Meta-analysis and meta-modelling for diagnostic problems
Meta-analysis and meta-modelling for diagnostic problems
Background

A proportional hazards measure is suggested in the context of analyzing SROC curves that arise in the meta–analysis of diagnostic studies. The measure can be motivated as a special model: the Lehmann model for ROC curves. The Lehmann model involves study–specific sensitivities and specificities and a diagnostic accuracy parameter which connects the two.

Methods

A study–specific model is estimated for each study, and the resulting study-specific estimate of diagnostic accuracy is taken as an outcome measure for a mixed model with a random study effect and other study-level covariates as fixed effects. The variance component model becomes estimable by deriving within-study variances, depending on the outcome measure of choice. In contrast to existing approaches – usually of bivariate nature for the outcome measures – the suggested approach is univariate and, hence, allows easily the application of conventional mixed modelling.

Results

Some simple modifications in the SAS procedure proc mixed allow the fitting of mixed models for meta-analytic data from diagnostic studies. The methodology is illustrated with several meta–analytic diagnostic data sets, including a meta–analysis of the Mini–Mental State Examination as a diagnostic device for dementia and mild cognitive impairment.

Conclusions

The proposed methodology allows us to embed the meta-analysis of diagnostic studies into the well–developed area of mixed modelling. Different outcome measures, specifically from the perspective of whether a local or a global measure of diagnostic accuracy should be applied, are discussed as well. In particular, variation in cut-off value is discussed together with recommendations on choosing the best cut-off value. We also show how this problem can be addressed with the proposed methodology.
diagnostic accuracy, mixed modelling, random effects modelling, cut-off value modelling, SROC modelling
1471-2288
1-13
Charoensawat, Suphada
a6dca895-a731-4700-ba04-674187ae2dd8
Bohning, Walailuck
4d2abe7f-ae5e-4df1-903f-086366664de6
Böhning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Holling, Heinz
88d46f56-77ca-4d0e-b035-a51aff735435
Charoensawat, Suphada
a6dca895-a731-4700-ba04-674187ae2dd8
Bohning, Walailuck
4d2abe7f-ae5e-4df1-903f-086366664de6
Böhning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Holling, Heinz
88d46f56-77ca-4d0e-b035-a51aff735435

Charoensawat, Suphada, Bohning, Walailuck, Böhning, Dankmar and Holling, Heinz (2014) Meta-analysis and meta-modelling for diagnostic problems. BMC Medical Research Methodology, 14 (56), 1-13. (doi:10.1186/1471-2288-14-56).

Record type: Article

Abstract

Background

A proportional hazards measure is suggested in the context of analyzing SROC curves that arise in the meta–analysis of diagnostic studies. The measure can be motivated as a special model: the Lehmann model for ROC curves. The Lehmann model involves study–specific sensitivities and specificities and a diagnostic accuracy parameter which connects the two.

Methods

A study–specific model is estimated for each study, and the resulting study-specific estimate of diagnostic accuracy is taken as an outcome measure for a mixed model with a random study effect and other study-level covariates as fixed effects. The variance component model becomes estimable by deriving within-study variances, depending on the outcome measure of choice. In contrast to existing approaches – usually of bivariate nature for the outcome measures – the suggested approach is univariate and, hence, allows easily the application of conventional mixed modelling.

Results

Some simple modifications in the SAS procedure proc mixed allow the fitting of mixed models for meta-analytic data from diagnostic studies. The methodology is illustrated with several meta–analytic diagnostic data sets, including a meta–analysis of the Mini–Mental State Examination as a diagnostic device for dementia and mild cognitive impairment.

Conclusions

The proposed methodology allows us to embed the meta-analysis of diagnostic studies into the well–developed area of mixed modelling. Different outcome measures, specifically from the perspective of whether a local or a global measure of diagnostic accuracy should be applied, are discussed as well. In particular, variation in cut-off value is discussed together with recommendations on choosing the best cut-off value. We also show how this problem can be addressed with the proposed methodology.

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More information

Accepted/In Press date: 14 April 2014
Published date: 24 April 2014
Keywords: diagnostic accuracy, mixed modelling, random effects modelling, cut-off value modelling, SROC modelling
Organisations: Statistics, Statistical Sciences Research Institute, Primary Care & Population Sciences

Identifiers

Local EPrints ID: 365547
URI: https://eprints.soton.ac.uk/id/eprint/365547
ISSN: 1471-2288
PURE UUID: af90baee-6c29-49ef-bc5a-51487c9eb949
ORCID for Dankmar Böhning: ORCID iD orcid.org/0000-0003-0638-7106

Catalogue record

Date deposited: 10 Jun 2014 08:46
Last modified: 20 Jul 2019 00:44

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