Standard models in evidence synthesis work well in settings characterized by a large evidence base, the absence of effect modifiers, and connected networks. Handling sparse data, substantial between-study heterogeneity and disconnected studies, however, poses challenges to researchers and requires advanced methodology.



In the absence of head-to-head studies, evidence synthesis is a well-established technique to indirectly compare novel and established interventions in various disease areas. The most established methods for various outcome types are described in the NICE Decision Support Unit (DSU) guidelines on evidence synthesis and the manual of the R package ‘netmeta’ for network meta-analysis. In standard settings, these methods work well and result in realistic effect estimates. However, there are a variety of situations when these standard methods may no longer be sufficient:

  • if there is only a sparse network of evidence (such as less than five studies informing one outcome, or only one study informing each link in the network)
  • if there is a large amount of between-study heterogeneity
  • if the network is disconnected

In this webinar, we will:

  • Give a general introduction into the objectives of conducting evidence synthesis
  • Describe typical situations of “non-standard” data, including sparse networks of evidence, a large amount of between-study heterogeneity, or disconnected networks
  • Present advanced methods to address non-standard data, including the use of informative priors, subgroup analyses, meta-regression and multi-level meta regression, and matching-adjusted indirect comparisons (MAICs)
  • Present case studies illustrating how these advanced methods of evidence synthesis are applied on actual data


This webinar is intended for professionals from pharmaceutical, biotech, and medical device companies involved in:

  • Epidemiology
  • Health Technology Assessment
  • Health economics & outcomes research
  • Marketing
  • Market access, pricing and reimbursement
  • Statistics
  • Real World Evidence


Katrin Haeussler, MSc, PhD

Katrin Haeussler, MSc, PhD

Senior Health Economist, ICON

Katrin works in the area of evidence synthesis, conducting analyses in both Bayesian and frequentist settings. She is experienced in implementing network meta-analysis models in R and SAS, and scientific writing. Therapeutic area experience includes chronic obstructive pulmonary disease, breast cancer, hereditary angioedema, diabetes and herpes zoster.

Matthias Hunger, MSc, Dr. rer. biol. hum

Matthias Hunger, MSc, Dr. rer. biol. hum

Lead Epidemiologist, ICON

Matthias is involved in all statistical components of research projects, from study design, protocol development, and data analysis to writing study reports or research manuscripts. He has extensive knowledge in the analysis of patient level data in SAS or R, including statistical analyses of health-related quality of life, utility, health care cost or survival data. His experience also encompasses innovative statistical methods of indirect comparisons, especially matching-adjusted indirect comparisons (MAIC) and synthetic control arm analyses.

Nathan Green, PhD

Nathan Green, PhD

Senior Research Fellow, University College London

Nathan has a number of years of experience working on a wide range of projects across government and academia in defence and health. He currently works in the Department of Statistical Science at UCL. His research interests include Bayesian statistical modelling for cost effectiveness analysis and decision-making problems in the health systems, hierarchical/multilevel models and causal inference using the decision-theoretic approach.