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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

365
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
365

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Related Experiment Video

Updated: Jul 14, 2025

A Bioluminescent and Fluorescent Orthotopic Syngeneic Murine Model of Androgen-dependent and Castration-resistant Prostate Cancer
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Cost-Effectiveness Analysis for Therapy Sequence in Advanced Cancer: A Microsimulation Approach with Application to

Elizabeth A Handorf1, J Robert Beck2, Andres Correa3

  • 1Rutgers University School of Public Health, Cancer Institute of New Jersey, USA.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|October 9, 2023
PubMed
Summary
This summary is machine-generated.

A new microsimulation model accurately predicts outcomes for advanced prostate cancer therapy sequences. The abiraterone acetate followed by docetaxel sequence offers better quality-adjusted life-years and lower costs compared to docetaxel followed by abiraterone acetate.

Keywords:
calibrationmicrosimulation modeltherapy sequence

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Area of Science:

  • Oncology
  • Health Economics
  • Biostatistics

Background:

  • Advanced cancer patients often require multiple treatment lines.
  • Therapy sequencing is crucial for optimizing patient outcomes.
  • Prostate cancer treatment involves sequential use of therapies like docetaxel and abiraterone acetate.

Purpose of the Study:

  • To develop a general microsimulation framework for studying cancer therapy sequences.
  • To apply this framework to metastatic prostate cancer treatment strategies.
  • To compare the cost-effectiveness of different therapy sequences.

Main Methods:

  • Developed a discrete-time state transition model.
  • Utilized digitized survival curves to estimate transition probabilities.
  • Incorporated within-patient dependence and calibrated the model to clinical trial data.
  • Assessed quality-adjusted life-years (QALYs) and costs for two sequences: docetaxel → abiraterone acetate (DCT → AA) and AA → DCT.

Main Results:

  • Models without within-patient dependence overestimated overall survival (OS).
  • The abiraterone acetate → docetaxel (AA → DCT) sequence dominated DCT → AA, showing higher 5-year QALYs and lower costs with generic pricing.
  • Model calibration improved the QALY difference and reduced the cost difference between strategies.
  • AA → DCT remained cost-effective over a lifetime horizon (ICER: $19,463).

Conclusions:

  • A microsimulation approach can effectively model therapy sequences in advanced prostate cancer.
  • Accounting for within-patient dependence is critical for accurate cost-effectiveness analysis.
  • The AA → DCT sequence appears more favorable than DCT → AA.