The work presented is from the IMI GetReal project: http://www.imi-getreal.eu/
1. Orestis Efthimiou (University of Ioannina): "Methods for combining randomized and non-randomized evidence in a network meta-analysis"
Network meta-analyses (NMAs) are commonly employed in comparative effectiveness assessments for comparing medicines used in clinical practice and for assessing the efficacy and safety of a new medicine relative to existing therapies. Applications of NMA are often limited to the synthesis of evidence coming from randomized controlled trials (RCTs), while non-randomized evidence is often disregarded. Observational studies, however, convey valuable information about the effectiveness of interventions in real-life clinical practice and in recent years there has been a growing interest for methods to include them in the decision-making process. In this workshop we will discuss existing approaches and we will present new methods for incorporating non-randomized evidence in a NMA of RCTs, highlighting the advantages, limitations and key challenges of each approach. We will illustrate our methods using a network of pharmacological treatments for schizophrenia.
2. Thomas Debray (University Medical Centre, Utrecht): "Network meta-analysis using IPD - an illustration of its potential advantages"
With increasing access to individual patient data (IPD), it is easier to incorporate patient level covariates in a network meta-analysis (NMA). The different statistical models for how to incorporate IPD into an NMA and the challenges this brings will be presented and discussed.
3. Eva-Maria Didden (University of Berne, Switzerland): "Learning and Predicting Real-World Treatment Effect based on Randomized Trials and Observational Data: A case study on rheumatoid arthritis"
Real world evidence can be incorporated with RCT data to predict effectiveness. An approach for how to model and predict effectiveness using efficacy outcomes from RCTs and outcomes observed in clinical practice will be presented.