The Cost-Effectiveness of Pertuzumab for the Treatment of Metastatic HER2+ Breast Cancer in Czechia: A Semi-Markov Model Using Cost States

帕妥珠单抗 多西紫杉醇 医学 转移性乳腺癌 危险系数 比例危险模型 乳腺癌 生存分析 肿瘤科 内科学 统计 癌症 置信区间 数学
作者
Lenka Šlegerová,Kateřina Kopečková
出处
期刊:Value in health regional issues [Elsevier BV]
卷期号:38: 118-125 被引量:1
标识
DOI:10.1016/j.vhri.2023.08.002
摘要

Objectives This article estimates the cost-effectiveness of adding pertuzumab to the combination of trastuzumab and docetaxel within the first-line treatment for metastatic breast cancer with the amplification of HER2+. Methods Data from Czech clinical practice recorded in the BREAST register are used. A semi-Markov model with states derived based on the treatment phases (first-line medication, no medication, next-line medication, death) is defined to estimate costs from the healthcare payers’ perspective. The benefits are estimated as patient survival until death. The Kaplan–Meier estimates are supplemented by the Cox proportional hazard and the accelerated failure time models to control for patient characteristics. Health-related quality-of-life indicators are derived from relevant literature. Results Based on the used data, adding pertuzumab does not result in statistically significantly longer survival while inducing higher treatment costs (€163 360 compared with €90 112 per patient in 2018 prices). Statistically longer survival was not supported by the log-rank test (P = .97), the Cox proportional hazard model, or the accelerated failure time model using the Gompertz distribution. The incremental cost-effectiveness ratio (€87 200) substantially exceeds the willingness to pay for 1 quality-adjusted life-year (€46 500). Conclusions This analysis indicates that adding pertuzumab cannot be considered cost-effective in Czechia. However, the observed phenomenon may be attributed to the limited duration of patient follow-up periods at the time of the study’s execution (mean of 20-21 months). Importantly, we find that using states connected to specific treatment phases is appropriate for a retrospective analysis of patient-level clinical data.
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