Methodological guidance, recommendations and illustrative case studies for (network) meta-analysis and modelling to predict real-world effectiveness using individual participant and/or aggregate data

Hummel N, Debray TPA, Didden E-M, Efthimiou O, Egger M, Fletcher C, Moons KG, Reitsma JB, Ruffieux Y, Salanti G, van Valkenhoef G, on behalf of WP4

This report presents best practices and recommendations for the area of evidence synthesis, in particular (network) meta-analysis (NMA) including aggregate and individual patient-level data (IPD) from randomized and non-randomized studies (NRS), modelling to predict effectiveness from efficacy data, and software.

Through a series of literature reviews, we summarize state-of-the art methods in

  • NMA,
  • IPD meta-analysis,
  • mathematical modelling to predict drug effectiveness based on randomized controlled trials (RCT) data,
and related software, and we discuss their advantages and limitations.

In three case studies, covering the disease areas of schizophrenia, depression and rheumatoid arthritis, we explore methods for NMA including IPD from NRS, NMA including IPD from RCT, and modelling to predict drug effectiveness, respectively. Based on these case studies, we develop recommendations on how to best conduct such analyses.

We provide recommendations when to include NRS into a NMA, when to include IPD into a NMA, and how to prioritize IPD retrieval. Furthermore, we stress the importance of seeking clinical expert advice and of model validation when building and running a model to predict drug effectiveness.

Although we could cover a broad range of evidence synthesis and prediction modelling methods with our case studies, further case study work and simulation studies are needed to evaluate the benefits and limitations of the proposed methods and to provide clear recommendations.