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Recalibrating Risk Prediction Models by Synthesizing Data Sources: Adapting the Lung Cancer PLCO Model for Taiwan.

Li-Hsin Chien1, Tzu-Yu Chen1, Chung-Hsing Chen2

  • 1Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.

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Summary

Researchers adapted a lung cancer risk model for Taiwan using existing data, improving health equity. The new PLCOT models show high predictive performance for lung cancer screening in the Taiwanese population.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Absolute risk models are crucial for personalized health decisions but often lack data for diverse populations.
  • Health disparities arise when risk models are not adapted for specific demographic and geographic contexts.
  • This study addresses the need for methods to adapt existing risk models without requiring new prospective cohorts.

Purpose of the Study:

  • To develop and validate methods for adapting western-developed absolute risk models for use in a different population (Taiwan) without new prospective data.
  • To adapt the Lung Cancer Risk Model PLCOM2012 for the Taiwanese population to improve lung cancer risk prediction and screening.
  • To demonstrate a generalizable approach for adapting risk models to reduce health disparities.

Main Methods:

  • Synthesized Taiwanese multiple data sources to form an age-matched case-control study of ever-smokers (AMCCSE).
  • Estimated the number of ever-smoking lung cancer patients (NESLP2011) and synthesized a dataset of cancer-free ever-smokers (SPES2010) for model calibration.
  • Utilized AMCCSE for calibration slope estimation and NESLP2011 against SPES2010 for calibration-in-the-large adjustment.

Main Results:

  • The adapted models, PLCOT-1 and PLCOT-2, demonstrated high predictive performance with AUCs of 0.78 and 0.75, respectively.
  • Models showed strong calibration and clinical utility across subgroups defined by age and smoking history within the Taiwanese population.
  • Screening the same number of individuals using PLCOT-1 (PLCOT-2) would identify approximately 6% (8%) more lung cancers compared to US guidelines.

Conclusions:

  • The adapted PLCOT models exhibit high predictive performance and are suitable for developing lung cancer screening programs in Taiwan.
  • The proposed methods for adapting risk models are generalizable and can be applied to other cancer types and populations.
  • This approach can help mitigate health disparities by making advanced risk prediction tools accessible globally.