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Semiparametric Partial Functional Regression Model for Estimating Optimal Individualized Treatment Regime.

Kaidi Kong1, Li Guan1, Zhongzhan Zhang1

  • 1School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing, China.

Statistics in Medicine
|December 15, 2025
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Summary

This study introduces a new semiparametric regression model for personalized medicine, improving treatment recommendations using patient data. The method enhances accuracy and interpretability for optimal individualized treatment regimes.

Keywords:
causal inferencefunctional dataindividualized treatment regimepersonalized medicinesemiparametric model

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Personalized Medicine

Background:

  • Personalized medicine aims to tailor treatments using patient data, including complex functional forms.
  • Accurate estimation of individualized treatment regimes is crucial for effective patient care.

Purpose of the Study:

  • To propose a novel semiparametric partial functional regression model for estimating optimal individualized treatment regimes.
  • To incorporate both scalar and functional patient covariates for more precise treatment recommendations.
  • To ensure model interpretability and reduce the risk of misspecification.

Main Methods:

  • Developed a semiparametric partial functional regression model with nonparametric main effects and flexible treatment interactions.
  • Employed B-spline estimation for model parameters.
  • Established theoretical convergence rates for the proposed estimation method.

Main Results:

  • The proposed model effectively estimates optimal individualized treatment regimes using diverse patient data.
  • The single index interaction with a monotone link function ensures interpretability.
  • Simulation studies and real data analysis demonstrate the method's strong performance.

Conclusions:

  • The novel semiparametric partial functional regression model offers a robust approach for personalized medicine.
  • This method enhances the estimation of optimal individualized treatment regimes by accommodating complex patient data.
  • The model provides a valuable tool for improving treatment decisions in clinical practice.