A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta‐analysis

@article{Debray2013AFF,
  title={A framework for developing, implementing, and evaluating clinical prediction models in an individual participant data meta‐analysis},
  author={Thomas P. A. Debray and Karel G. M. Moons and Ikhlaaq Ahmed and Hendrik Koffijberg and Richard D. Riley},
  journal={Statistics in Medicine},
  year={2013},
  volume={32}
}
The use of individual participant data (IPD) from multiple studies is an increasingly popular approach when developing a multivariable risk prediction model. Corresponding datasets, however, typically differ in important aspects, such as baseline risk. This has driven the adoption of meta‐analytical approaches for appropriately dealing with heterogeneity between study populations. Although these approaches provide an averaged prediction model across all studies, little guidance exists about how… 
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153 Citations
Highly Influential Citations
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Background Citations
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Methods Citations
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Results Citations
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153 Citations

Developing and validating risk prediction models in an individual participant data meta-analysis

An IPD meta-analysis offers unique opportunities for risk prediction research by allowing separate model intercept terms for each study (population) to improve generalisability, and by using ‘internal-external cross-validation’ to simultaneously develop and validate their model.

Meta‐analysis and aggregation of multiple published prediction models

Model aggregation is a promising strategy when several prediction models are available from the literature and a validation dataset is at hand, and results in equivalent performance when validation datasets are relatively large.

Individual participant data meta‐analysis for external validation, recalibration, and updating of a flexible parametric prognostic model

IPD meta-analysis allows a prognostic model to be externally validated in multiple settings, and enables recalibration strategies to be compared and ranked to decide on the least aggressive recalibrations strategy to achieve acceptable external model performance without discarding existing model information.

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model

Meta-analysis of risk prediction studies

A systematic review of published breast cancer models is performed, and IPD is obtained from an article which uses parathyroid hormone (PTH) assay (a continuous variable) to predict postoperative hypocalcaemia after thyroidectomy, and results indicate the approach performs well, though further research is required.

Evidence synthesis for prognosis and prediction : application, methodology and use of individual participant data

This thesis investigates the application and development of evidence synthesis methods for prognosis research, with particular attention given to improving individualised predictions from prognostic models developed and/or validated using metaanalysis techniques.

Multiple Score Comparison: a network meta-analysis approach to comparison and external validation of prognostic scores

This work applies a network meta-analytic methodology to externally validate and concurrently compare the prognostic properties of clinical scores, and indicates that the scores with the best discriminative properties to predict 3 year mortality in patients with COPD are ADO and eBODE.

Developing more generalizable prediction models from pooled studies and large clustered data sets

The methodology enables the assessment of heterogeneity of prediction model performance during model development in multiple or clustered data sets, thereby informing researchers on predictor selection to improve the generalizability to different settings and populations, and reduce the need for model tailoring.

Assessment of heterogeneity in an individual participant data meta‐analysis of prediction models: An overview and illustration

Approaches for assessing heterogeneity in predictor effects and predictions arising from models based on data from different sources are proposed and illustrated in a case study with patients suffering from traumatic brain injury.

A review of statistical updating methods for clinical prediction models

A clinical prediction model is a tool for predicting healthcare outcomes, usually within a specific population and context. A common approach is to develop a new clinical prediction model for each…
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