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Area of Science:

  • Health economics
  • Statistical modeling
  • Decision science

Background:

  • Cost-effectiveness analysis (CEA) typically uses population-level data.
  • Individualized treatment rules (ITRs) can enhance CEA by accounting for patient heterogeneity.
  • Statistical learning offers novel methods for designing ITRs from simulated data.

Purpose of the Study:

  • To propose and evaluate a statistical learning approach for designing individualized treatment rules (ITRs).
  • To optimize cost-effectiveness by tailoring treatments to individual patient characteristics using microsimulation models.
  • To compare the performance of different statistical learning techniques for ITR derivation.

Main Methods:

  • Defined optimal ITRs and their value in cost-effectiveness.
  • Applied LASSO regression, classification trees, and policy trees to simulated patient data.
  • Compared methods based on incremental net monetary benefit (NMB), computational speed, and interpretability.
  • Assessed the impact of parameter and stochastic uncertainty on ITRs.
  • Illustrated methods using a microsimulation model for haemophilia B.

Main Results:

  • A two-layer classification tree was found to be the most suitable method based on defined criteria.
  • The optimal classification tree allocated treatments based on thresholds for annualized bleeding rate and age.
  • Threshold values exhibited uncertainty, with 95% credible ranges identified.
  • Stochastic uncertainty was shown to impact the incremental value of ITRs.

Conclusions:

  • Classification trees are expected to be superior for ITR derivation in similar microsimulation models.
  • Accurate patient pathway representation is crucial due to the significant impact of stochastic uncertainty on ITRs.
  • Future research should explore further empirical models and real-world application of ITRs.