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. 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 will mainly focus on meta-analysis of the performance of a prognostic model. 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. We illustrate this with various empirical examples. In the second part, participants will work in small groups to interpret the findings of a published meta-analysis.