Article type
Year
Abstract
Background:
The development and (external) validation of diagnostic and prognostic prediction models is an important aspect of contemporary epidemiological research. Unfortunately, many prediction models perform more poorly than anticipated when tested or applied in other individuals, and interpretation of their generalizability is not straightforward. During the past decades, evidence synthesis and meta-analysis of individual participant data (IPD) have become increasingly popular for improving the development, validation and eventual performance of novel prediction models. Also, IPD meta-analysis lead to a better understanding in the generalizability of prediction models across different populations. There is, however, little guidance on how to conduct an IPD meta-analysis for developing and validating diagnostic or prognostic prediction models
Objective and Methods:
We provide guidance for both authors and reviewers in appraising IPD meta-analyses that aim to develop and/or validate a prediction model using multiple IPD datasets. Furthermore, we demonstrate why and how IPD meta-analysis of risk prediction research differs from IPD meta analysis of intervention research. Finally, we provide methodological recommendations for conducting an IPD meta-analysis for risk prediction research, and illustrate these with a clinical example.
Conclusions:
Whereas meta-analytical strategies for intervention research have been well described during the past few decades, evidence synthesis in risk prediction research is relatively new. Appropriate methods for conducting an IPD meta-analysis in risk prediction research have become available during the past few years, and clearly differ from their counterparts in intervention research.
The development and (external) validation of diagnostic and prognostic prediction models is an important aspect of contemporary epidemiological research. Unfortunately, many prediction models perform more poorly than anticipated when tested or applied in other individuals, and interpretation of their generalizability is not straightforward. During the past decades, evidence synthesis and meta-analysis of individual participant data (IPD) have become increasingly popular for improving the development, validation and eventual performance of novel prediction models. Also, IPD meta-analysis lead to a better understanding in the generalizability of prediction models across different populations. There is, however, little guidance on how to conduct an IPD meta-analysis for developing and validating diagnostic or prognostic prediction models
Objective and Methods:
We provide guidance for both authors and reviewers in appraising IPD meta-analyses that aim to develop and/or validate a prediction model using multiple IPD datasets. Furthermore, we demonstrate why and how IPD meta-analysis of risk prediction research differs from IPD meta analysis of intervention research. Finally, we provide methodological recommendations for conducting an IPD meta-analysis for risk prediction research, and illustrate these with a clinical example.
Conclusions:
Whereas meta-analytical strategies for intervention research have been well described during the past few decades, evidence synthesis in risk prediction research is relatively new. Appropriate methods for conducting an IPD meta-analysis in risk prediction research have become available during the past few years, and clearly differ from their counterparts in intervention research.