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Related Experiment Videos

Survival curve fitting using the Gompertz function: a methodology for conducting cost-effectiveness analyses on

A Messori1

  • 1Meta-analysis Study Group of SIFO, Azienda Ospedaliera Careggi, Florence, Italy.

Computer Methods and Programs in Biomedicine
|March 1, 1997
PubMed
Summary

Analyzing survival curves helps compare treatment costs and effectiveness. This method estimates patient years gained, enabling cost-per-life-year saved calculations for new, more expensive therapies.

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Area of Science:

  • Pharmacoeconomics
  • Health Economics
  • Biostatistics

Background:

  • Incremental cost-effectiveness analysis (ICER) is crucial for comparing new treatments against standard ones.
  • Estimating patient years from survival curves is essential for ICER calculations.
  • Existing methods for survival curve analysis can be complex and computationally intensive.

Purpose of the Study:

  • To present a novel survival-curve fitting method for estimating patient years.
  • To introduce a computer program that implements this pharmacoeconomic methodology.
  • To facilitate accurate cost-per-life-year gained calculations.

Main Methods:

  • Utilized non-linear least-squares analysis to fit survival data to the Gompertz function.
  • Employed the Gompertz survival function integration to estimate the area under the curve (AUC).

Related Experiment Videos

  • Calculated incremental patient years gained by comparing AUCs of treatment and control groups.

    • Main Results:

    • The method successfully estimates total patient years from survival curve data.
    • The difference in AUCs between groups quantifies incremental patient years gained.
    • This approach enables the calculation of incremental cost-effectiveness ratios.

    Conclusions:

    • The described survival-curve fitting method provides a robust way to estimate patient years for cost-effectiveness analysis.
    • The integrated approach, using the Gompertz function and AUC, simplifies pharmacoeconomic evaluations.
    • This methodology supports informed decision-making regarding the adoption of new, more effective treatments.