Background: prediction models are commonly developed and validated for predicting the presence (diagnostic) or future occurrence (prognostic) of a particular outcome. Prediction models have become abundant in the literature. Many models have been validated in numerous different studies/publications. Also, numerous studies investigate the (added) value of a prognostic factor/predictor/biomarker to existing predictors. In both situations, aggregating such data is important for making inferences on the predictive performance of a specific model or predictor/marker. Meta-analytical approaches for both situations have recently been developed.

Objectives: this workshop introduces participants to statistical methods for meta-analysis in systematic reviews of prognosis studies. We address both meta-analysis of the accuracy of a prognostic model and of the (added) predictive value of a prognostic factor. We discuss opportunities/challenges of the statistical methods and of common software packages.

Description: in this workshop we illustrate these statistical approaches and how to combine, quantitatively, results from published studies on the predictive accuracy of a prognostic model or (added) predictive accuracy of a prognostic factor. We illustrate this with various empirical examples.