BMC medical research methodology · 2014
Meta-analysis and meta-modelling for diagnostic problems.
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Charoensawat Suphada, Böhning Walailuck, Böhning Dankmar, Holling Heinz
Аннотация
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.
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.