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Using risk prediction models to inform personalized, cost-effective treatment recommendations.

Mariana R Neves1, Molly Franke2, Carole Mitnick2

  • 1Department of Health Policy and Management, Yale School of Public Health, New Haven, USA.

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Summary

New methods integrate disease risk prediction with decision modeling for personalized, cost-effective treatment choices. This approach improves health outcomes and resource use, especially when diagnostic tests are unavailable.

Keywords:
Clinical Decision SupportCost-effectivenessDecision Making under UncertaintyRisk Prediction ModelingTuberculosis

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Area of Science:

  • Health economics
  • Clinical decision-making
  • Biostatistics

Background:

  • Diagnostic uncertainty necessitates reliance on clinical judgment and prediction models.
  • Existing prediction models often overlook downstream health and cost implications.
  • Personalized treatment requires integrating risk assessment with decision analysis.

Purpose of the Study:

  • To develop and evaluate methods for integrating risk prediction with decision modeling.
  • To inform personalized and cost-effective treatment recommendations.
  • To maximize population net monetary benefit (NMB) by considering health and cost outcomes.

Main Methods:

  • Two integration methods were developed: probability-based and classification-based.
  • These methods were applied to optimize treatment selection for rifampicin-resistant tuberculosis.
  • The analysis accounted for regimen costs, toxicity, and efficacy.

Main Results:

  • Both integration methods improved population NMB compared to standard care and fixed thresholds.
  • The classification-based approach demonstrated robustness to prediction model calibration.
  • The study highlights the value of integrated models in resource-constrained settings.

Conclusions:

  • Integrating risk prediction with decision models provides a framework for value-based treatment decisions.
  • These methods enhance care quality by accounting for health and cost consequences.
  • The approach is particularly beneficial in situations with diagnostic uncertainty.