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Regression methods for cost-effectiveness analysis with censored data.

Andrew R Willan1, D Y Lin, Andrea Manca

  • 1Program in Population Health Sciences, Research Institute, Hospital for Sick Children, 555 University Avenue, Toronto, ON, Canada M5G 1X8. willana@mcmaster.ca

Statistics in Medicine
|October 30, 2004
PubMed
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This study introduces a new regression method for cost-effectiveness analysis with censored data. It improves prognostic adjustment and subgroup analysis for better healthcare economic evaluations.

Area of Science:

  • Health economics
  • Biostatistics
  • Survival analysis

Background:

  • Cost-effectiveness analysis (CEA) is crucial for healthcare decision-making.
  • Censored data in CEA presents challenges for accurate prognostic adjustment and subgroup analysis.
  • Dependent censoring on costs and quality-adjusted survival complicates standard methods.

Purpose of the Study:

  • To propose a novel statistical system for prognostic factor adjustment in CEA.
  • To enable robust subgroup analysis in the presence of censored data.
  • To address dependent censoring in cost and effectiveness measures.

Main Methods:

  • A system of seemingly unrelated regression equations is developed.
  • Inverse probability weighting is employed for parameter estimation.

Related Experiment Videos

  • The method is validated using survival time and quality-adjusted survival time.

    • Main Results:

    • The proposed method effectively handles prognostic factor adjustment.
    • Subgroup analyses are feasible and reliable with censored data.
    • Dependent censoring is appropriately managed through inverse probability weighting.

    Conclusions:

    • The novel regression system offers a significant advancement for CEA with censored data.
    • This approach enhances the accuracy of subgroup comparisons in economic evaluations.
    • The method provides a valuable tool for healthcare decision-makers.