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Modelling of ROC curves in meta-analysis of diagnostic test accuracy studies and network meta-analysis

Funding: Since 2012

Principal Investigator: Dr. Gerta Rücker (IMBI)


This project refers to two active research areas of evidence synthesis in medicine, meta-analysis of diagnostic test accuracy studies and network meta-analysis. The final objective is to combine both areas.In standard approaches of meta-analysis of diagnostic accuracy studies, each study is assumed to contribute one pair of sensitivity and specificity. In the first period of the project, we investigated two more general approaches, which account for multiple thresholds of the underlying biomarker, `Modelling biomarker distributions' and `Averaging ROC curves'.The first approach is based on the idea of estimating the distribution functions of an underlying biomarker within the non-diseased and diseased individuals. Based on a distributional assumption, we estimate the distribution parameters in the two groups applying a linear mixed effects model to the appropriately transformed data. The model accounts for both the within-study dependence of sensitivity and specificity and between-study heterogeneity. We obtain a summary receiver operating characteristic (SROC) curve as well as the pooled sensitivity and specificity at every specific threshold. Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index.In the second approach, originally introduced by Martinez-Camblor (2014), the summary ROC curve is determined as a weighted average of the ROC curves of the primary studies by averaging in `vertical' direction (i.e., averaging sensitivities, conditional on specificity). We extended this method by (1) exchanging the roles of sensitivity and specificity, i.e., averaging specificities conditional on sensitivity (horizontal averaging), and (2) averaging the differences of true positive rates and false positive rates (i.e., the Youden indices) conditional on their sum (diagonal averaging).Another area of research was network meta-analysis (NMA). In four publications, we investigated (1) the relation between NMA and electrical network theory, (2) an alternative method of adjusting for multi-arm studies by appropriately inflating the standard errors, (3) methods of automated visualisation of networks, and (4) frequentist treatment ranking, based on a network-meta-analysis.In the second project period we want to refine and extend all these approaches, with a special focus on knowledge translation. There is a more and more increasing spectrum of advanced methods on the one hand and their restricted accessibility for non-statistical users on the other hand. We want to bridge this gap and continue to follow this aim by writing a new R package for implementation of the `Modelling biomarker distributions' and `Averaging ROC curves' approaches and extending our existing R package netmeta for network meta-analysis, for example by meta-regression. The final objective is to combine meta-analysis of DTA studies and NMA to network meta-analysis of DTA studies.