New methods for RWE collection and synthesis

Welcome to GetReal

Workpackage 4

Identifying and sharing best practice in evidence synthesis and predictive modelling of different types of data to estimate effectiveness.

Context

There is a need to better understand how the results from randomised controlled trials (RCTs) can be used alongside other sources of clinical data, for example individual and aggregate data from observational studies and disease registries, to improve estimates of the real world effectiveness of medicines.

Aim

The general aim of WP4 is to identify and develop best practice in evidence synthesis and predictive modelling, to improve estimates of the real world effectiveness of medicines by incorporating the results of RCTs with other sources of clinical data, including observational data.

Key activities

The key activities of WP4 are:
  • Conduct literature reviews of methods for evidence synthesis and predictive modelling to identify best practice.
  • Test methods for evidence synthesis and predictive modelling using case studies:
    • Schizophrenia
    • Depression
    • Rheumatoid Arthritis
    • Cancer
  • Develop evidence synthesis and predictive modelling techniques and software.
Key outputs

One of the key outputs of WP4 is the development of comprehensive and user-friendly evidence synthesis and predictive modelling techniques and software and relevant training material to support best practice.

WP4 project leaders: WP4 project leaders:

Christine Fletcher, Amgen
Matthias Egger, ISPM