Article type
Year
Abstract
Background:
The development and validation of prediction models is an important area in contemporary medical research. Over the past few years, evidence synthesis and meta-analysis of individual participant data (IPD) has become increasingly popular, not only for intervention research but also for improving the development, validation and generalisability of prediction models. IPD meta-analyses (IPD-MA) provide unique opportunities to better develop and enhance the applicability of prediction models across (sub)populations and settings. There is, however, little guidance on how to conduct an IPD-MA aimed at developing and validating diagnostic and prognostic prediction models, and how to interpret the findings.
Objectives:
This workshop introduces IPD meta-analysis in risk prediction research and the required statistical methodology. We will illustrate the implementation of IPD meta-analysis using case studies and example papers.
Description:
We will discuss how IPD-MA aimed at developing and validating prediction models differ from IPD-MA for assessing treatment effects. We will identify key advantages and challenges in IPD-MA of prediction models. We will provide recommendations for the design of such IPD-MA including the selection of relevant studies. We will discuss statistical methods for handling between-study heterogeneity and other issues regarding prediction model development and validation. We will illustrate this using various empirical examples across medical disciplines.
The development and validation of prediction models is an important area in contemporary medical research. Over the past few years, evidence synthesis and meta-analysis of individual participant data (IPD) has become increasingly popular, not only for intervention research but also for improving the development, validation and generalisability of prediction models. IPD meta-analyses (IPD-MA) provide unique opportunities to better develop and enhance the applicability of prediction models across (sub)populations and settings. There is, however, little guidance on how to conduct an IPD-MA aimed at developing and validating diagnostic and prognostic prediction models, and how to interpret the findings.
Objectives:
This workshop introduces IPD meta-analysis in risk prediction research and the required statistical methodology. We will illustrate the implementation of IPD meta-analysis using case studies and example papers.
Description:
We will discuss how IPD-MA aimed at developing and validating prediction models differ from IPD-MA for assessing treatment effects. We will identify key advantages and challenges in IPD-MA of prediction models. We will provide recommendations for the design of such IPD-MA including the selection of relevant studies. We will discuss statistical methods for handling between-study heterogeneity and other issues regarding prediction model development and validation. We will illustrate this using various empirical examples across medical disciplines.