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

  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},
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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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.

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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.

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This first article focuses on the different aspects of model development studies, from design to reporting, how to estimate a model's predictive performance and the potential optimism in these estimates using internal validation techniques, and how to quantify the added or incremental value of new predictors or biomarkers to existing predictors.

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An overview is provided of the consecutive steps for the assessment of the model's predictive performance in new individuals, how to adjust or update existing models to local circumstances or with new predictors, and how to investigate the impact of the uptake of prediction models on clinical decision-making and patient outcomes (impact studies).

Construction and validation of a prognostic model across several studies, with an application in superficial bladder cancer

It is shown that a three-group prognostic classification scheme based on either score produces well-separated survival curves for each of the data sets, despite identifiable heterogeneity among the baseline distribution functions and to a lesser extent among the prognostic indexes for the individual studies.

Individual patient data meta-analysis of survival data using Poisson regression models

The approach provides a highly flexible and computationally efficient framework, available in all standard statistical software, to the investigation of not only heterogeneity, but the presence of non-proportional hazards and treatment effect modifiers.

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An evaluation algorithm for systematic assessment of the observed heterogeneity in disease risk within trial populations is presented and may be used for comparing diverse trials and used prospectively for designing future trials, as shown in simulations.

Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve.

  • N. Cook
  • Medicine
    Clinical chemistry
  • 2008
Evaluation of prognostic models should not rely solely on the ROC curve, but should assess both discrimination and calibration, to aid in comparing the clinical impact of two models on risk for the individual, as well as the population.

Clinical Prediction Models: A Practical Approach to Development, Validation and Updating

Suggested that could be demonstrated a live birth and data and demonstrated that were excluded, and developed and could be appropriate aac evidence.