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Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

346
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...
346
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

186
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
186
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  11. Comparison Of Partitioned Survival Modeling With State Transition Modeling Approaches With Or Without Consideration Of Brain Metastasis: A Case Study Of Osimertinib Versus Pemetrexed-platinum

Comparison of partitioned survival modeling with state transition modeling approaches with or without consideration of brain metastasis: a case study of Osimertinib versus pemetrexed-platinum

Yoon-Bo Shim1, Byeong-Chan Oh1, Eui-Kyung Lee2

  • 1School of Pharmacy, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi- do, Republic of Korea.

BMC Cancer
|February 9, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

Comparing economic models for advanced non-small cell lung cancer, the 5-state model (5-STM) showed higher life-years but lower quality-adjusted life-years (QALYs) when including brain metastases. Model choice significantly impacts QALY estimates.

Area of Science:

  • Health economics
  • Pharmacoeconomics
  • Oncology

Background:

  • Partitioned survival models (PSM) and state transition models (STM) are key for anticancer drug cost-effectiveness analyses.
  • Advanced non-small cell lung cancer (NSCLC) with EGFR mutations presents complex treatment and economic evaluation challenges.

Purpose of the Study:

  • To compare quality-adjusted life-year (QALY) estimates for Osimertinib versus pemetrexed-platinum in advanced NSCLC using different economic models.
  • To assess the impact of incorporating brain metastasis states into economic models.

Main Methods:

  • Three economic models were developed: PSM, a 3-state STM (3-STM), and a 5-state STM (5-STM) including brain metastasis.
  • Parametric curves fitted to patient-level claims data (2009-2020) informed time-dependent transition probabilities.
Keywords:
Cost-effectiveness analysisMarkov modelPartitioned survival modelQuality-adjusted life-year

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  • Incremental life-year (LY) and QALY were estimated over seven years for each model.

    • Main Results:

    • PSM and 3-STM yielded similar incremental LY (0.889-0.899) and QALY (0.827-0.840).
    • The 5-STM, accounting for brain metastasis, showed higher incremental LY (0.910) but a lower incremental QALY (0.695).

    Conclusions:

    • Incorporating specific health states like brain metastases significantly influences QALY estimates in economic models.
    • Justification and comparison of different modeling approaches are crucial for robust health technology assessments of anticancer drugs.