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

Valentijn M T de Jong1,2, Karel G M Moons1,2, Marinus J C Eijkemans1

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Summary
This summary is machine-generated.

Developing new prediction models requires methods to reduce heterogeneity across diverse populations and settings. This study introduces a novel approach using internal-external cross-validation and predictor selection to enhance model generalizability, minimizing the need for local adjustments.

Keywords:
heterogeneityindividual participant datainternal-external cross-validationprediction

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Informatics

Background:

  • Prediction models often perform poorly for new individuals due to heterogeneity in predictor-outcome associations across settings.
  • Existing model development strategies do not adequately address this heterogeneity, limiting generalizability and requiring local revisions.
  • Large datasets from pooled studies or electronic health records can increase sample size but do not inherently solve generalizability issues.

Purpose of the Study:

  • To develop and validate a methodology for creating prediction models with improved generalizability across different settings and populations.
  • To reduce the need for extensive local tailoring of prediction models.
  • To assess and minimize heterogeneity in prediction model performance during development.

Main Methods:

  • Internal-external cross-validation was employed to evaluate and mitigate performance heterogeneity.
  • A novel predictor selection algorithm was proposed to optimize average performance while minimizing variability across hold-out clusters.
  • The methodology was illustrated using individual participant data from cohorts predicting atrial fibrillation and diagnostic studies for deep vein thrombosis.

Main Results:

  • Meta-analysis of calibration and discrimination performance across hold-out clusters revealed trade-offs between average performance and its heterogeneity.
  • The proposed predictor selection iteratively added predictors to optimize generalizability.
  • The methodology successfully assessed heterogeneity during model development in multiple/clustered datasets.

Conclusions:

  • The developed methodology allows for the assessment of prediction model performance heterogeneity during development in multi-site or clustered data.
  • It guides predictor selection to enhance generalizability across diverse settings and populations.
  • This approach reduces the necessity for local model recalibration and has been implemented in the R package 'metamisc'.