Selected project overview
Network meta-analysis of healthcare interventions with different dosages and combinations of components
The objective of this DFG project is to enhance medical decision-making in intricate scenarios, including situations involving multicomponent interventions and dose-response relationships. This goal will be achieved through the development of an innovative model that integrates dose-response relationships into a network of interventions. To facilitate practical application, model diagnostics will be formulated, encompassing tests, quantification, and visualization methods to assess the fit of dose-response functions. For comparative analysis, a categorization approach will be employed, distinguishing between low, recommended, and high doses. These findings will serve as the foundation for enabling even more adaptable models when integrating dose-response models into a network of multicomponent interventions. The methodologies will be incorporated as add-ons to the widely utilized R package netmeta, aiming to facilitate broad acceptance within the meta-analysis community.
Person responsible: Maria Petropoulou
Participation: Guido Schwarzer, Gerta Rücker
Enabling New Types of Questions in Evidence Synthesis
The PICO scheme has been used for over two decades to formalize the research question of a systematic review, by helping to specify the population, interventions, comparators, and outcomes of interest. Many research questions asked today, however, are broader and more complex, and thus do not fit into the PICO scheme. While some methods have been suggested that could potentially enable evidence synthesis for parts of such questions, available methodological developments are rather disjoint and not well aligned, hindering their combined use in real world examples. The aim of this project is to broaden the PICO scheme and corresponding statistical methods by creating, evaluating and establishing a holistic framework for synthesizing, assessing and presenting evidence from systematic reviews. Developments will be stimulated by a range of research questions in several clinical areas. Our starting point will be existing methods to synthesize evidence with broad definitions of populations, treatment comparisons and outcomes, such as network meta-analysis, population adjustment methods, multivariate meta-analysis, and multiple criteria decision analysis. We will integrate these methods, extend them and enrich them with novel approaches to create a framework that will allow researchers to ask new types of questions in evidence synthesis. .
Person responsible: Adriani Nikolakopoulou
Participation: Theodoros Papakonstantinou, Guido Schwarzer, Gerta Rücker
SoftMeta – Software for Meta-Analysis
The goal of this project is the implementation and distribution of statistical methods for meta-ananalyses in the freely available software R. Starting point of the project are the R-packages meta, metasens, netmeta and diagmeta, which are available on GitHub. You can find further information on our project page at ResearchGate and the corresponding website to our course book "Meta-Analysis with R."
Person responsible: Guido Schwarzer
Participation: Gerta Rücker
Modelling of ROC curves in meta-analyses of diagnostic test accuracy studies and network meta-analysis
This DFG (German Research Foundation) project comprises two current research areas in the field of evidence synthesis in medicine: the meta-analysis of diagnostic test accuracy studies and network meta-analysis. We developed a new approach to the meta-analysis of diagnostic test accuracy studies that allows the pooling of entire ROC curves and additionally implemented a corresponding new R package called diagmeta. In the area of network meta-analysis we continuously add new methods to our R package netmeta, f.e. to separate the individual effects in combination therapies. The final objective is to combine both areas - network meta-analysis of diagnostic test accuracy studies.
Person responsible: Gerta Rücker
Participation: Guido Schwarzer, Susanne Steinhauser (diagnostics)