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A Comparison of Methods to Detect Changes in Prediction Models.

Erin M Schnellinger1, Wei Yang1, Michael O Harhay1

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Detecting changes in medical prediction models is crucial. Calibration regression often detects changes faster than the Direct Approach, especially for incidence shifts, while the Direct Approach may be better for covariate changes.

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Area of Science:

  • Medical Statistics
  • Health Informatics
  • Clinical Epidemiology

Background:

  • Medical prediction models are widely used but often assume static parameters.
  • Model coefficients can drift over time due to evolving patient characteristics and disease risks.
  • Optimal methods for detecting these parameter changes remain underexplored.

Purpose of the Study:

  • To evaluate and compare two methods for detecting changes in medical prediction model parameters over time.
  • To assess the performance of the Direct Approach and Calibration Regression under various simulated scenarios.

Main Methods:

  • Simulated post-lung transplant mortality data using logistic regression for binary outcomes.
  • Tested the Direct Approach by refitting models on recent data and comparing coefficients.
  • Tested Calibration Regression by modeling observed outcomes against baseline model predictions, assessing intercept and slope deviations.

Main Results:

  • Calibration Regression demonstrated earlier change detection and better performance (e.g., higher true positive rates) compared to the Direct Approach.
  • Both methods performed well with large sample sizes.
  • Neither method effectively detected simultaneous changes in two parameters.

Conclusions:

  • No single method is universally optimal for detecting changes in prediction model parameters.
  • Calibration Regression may be superior for detecting changes in outcome incidence (intercept).
  • The Direct Approach may be more suitable for detecting changes in model covariates (slope).