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Developing risk models for multicenter data using standard logistic regression produced suboptimal predictions: A

Nora Falconieri1, Ben Van Calster1,2, Dirk Timmerman1,3

  • 1Department of Development and Regeneration, KU Leuven, Leuven, Belgium.

Biometrical Journal. Biometrische Zeitschrift
|January 21, 2020
PubMed
Summary
This summary is machine-generated.

For multicenter clinical prediction models, random intercept logistic regression offers reliable predictions, especially with larger sample sizes. Standard logistic regression may miscalibrate, particularly with clustered data.

Keywords:
calibrationdiscriminationmulticenterrandom effectsrisk prediction model

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Health Services Research

Background:

  • Multicenter data are frequently used in clinical prediction model development.
  • Ignoring data clustering from multiple centers can lead to suboptimal model performance.
  • Standard statistical methods may not adequately account for hierarchical data structures.

Purpose of the Study:

  • To evaluate the predictive performance of various regression methods for clinical risk models using multicenter data.
  • To compare standard logistic regression, generalized estimating equations, random intercept logistic regression, and fixed effects logistic regression.
  • To provide practical guidelines for developing prediction models with clustered multicenter data.

Main Methods:

  • A case study on ovarian cancer diagnosis was used for illustration.
  • A simulation study assessed model performance under varying conditions (clustering, sample size, center effects).
  • Evaluated conditional models (standard logistic regression, fixed effects logistic regression) and marginal models (generalized estimating equations, random intercept logistic regression).

Main Results:

  • Sufficiently large sample sizes yielded calibrated predictions with conditional models, while marginal models showed miscalibration.
  • Small sample sizes resulted in overfitting and unreliable predictions, with miscalibration worsening in highly clustered data.
  • Random intercept logistic regression demonstrated superior calibration compared to standard logistic regression across various challenging scenarios.

Conclusions:

  • Random intercept logistic regression is recommended for developing reliable clinical prediction models using multicenter data.
  • Model choice significantly impacts prediction accuracy and calibration, particularly with clustered data.
  • Careful consideration of data structure and sample size is crucial for robust clinical prediction model development.