Systematic reviews of prognostic studies I: Design, protocol and data extraction


Workshop session 1: Wednesday, 11:00-12:30

Workshop category: 

  • Methods for conducting syntheses (including different evidence, searching and information retrieval, statistics, assessing methodological quality)


Date and Location


Wednesday 13 September 2017 - 11:00 to 12:30


Contact persons and facilitators

Contact person:


Carl Moons
Lotty Hooft
Katrina Williams
Target audience

Target audience: 

Reviewers with an interest in systematic reviews of prognosis studies

Level of difficulty: 

Type of workshop

Type of workshop : 



Objectives: This workshop will introduce participants to the design, conduct, data extraction and critical appraisal in systematic reviews of prediction-modelling studies.
Description:We will discuss and provide guidance on how to define a proper review question and how to design your data-extraction form to enhance critical appraisal of primary prediction-modelling studies. We will illustrate this using real examples.
Prediction models are developed and validated for predicting current or future occurrence of a particular outcome. Publications on prediction models are abundant. Hence, systematic reviews of these studies are increasingly required and conducted, to identify and critically appraise the existing evidence. Recently a tool has been developed to provide guidance for design and conduct of systematic reviews of studies developing and/or validating prediction models, that can assist reviewers to define the review objectives, to design the review and the data extraction list to facilitate appraisal of the primary studies. We discuss the key items important for framing the review question, and the domains with corresponding signaling items for data extraction and thus for critical appraisal.
We discuss the CHARMS checklist; developed to assist reviewers in framing their review objective, to design their review, and to formulate their data-extraction list to facilitate critical appraisal of the primary studies on development and/or validation of prediction models.