Meta-analysis of the validity of binary diagnoses based on dichotomized cirteria

• Applicant: Professor Dr. Heinz Holling
• Subject Area: General, Cognitive and Mathematical Psychology
• Term: from 2008 to 2017
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 58856953

In the first phase of the project two new meta-analytic models for diagnostic studies were developed that explicitly incorporated the cut-off value problem. The first model, a proportional hazards model (PHM), is based upon the Lehmann-family for SROC curves, whereas the second model uses a bivariate normal distribution of logarithmically transformed sensitivities and specificities. In the second phase of this project, these models were extended to allow for covariate information, to explain heterogeneity in the random effects model. Furthermore, a parametric link function was included in the PHM. Hence this approach is embedded in a larger class of models with rather flexible SROC curves. To make these methodologies readily available for a larger user community the R-package mada has been developed.The models developed so far allow the analysis of observed heterogeneity (by means of covariates) as well as unobserved heterogeneity (by means of random effect models). In this third phase, for which one year is available, unobserved heterogeneity will be further quantified. As the diversity of collected meta-analyses shows, almost every available meta-analysis of diagnostic studies shows some form of unobserved heterogeneity. However, the strength of heterogeneity varies considerably across the various meta-analyses. Conventionally, the magnitude of heterogeneity is measured by means of the heterogeneity measure I2, suggested by Higgins und Thompson (2002). This quantity, however, is not a classical effect measure as it is not independent of the sample sizes of the individual studies. Here, a modified heterogeneity measure is suggested for diagnostic meta-analyses, which does not suffer under the above limitation. The development of this new heterogeneity measure has emerged through the findings of the second phase of the project. Furthermore, the properties and qualities of this new heterogeneity measure will be investigated analytically and by means of simulation studies.

• DFG Programme: Research Grants