2017 Volume 40 Issue 1 Pages 73-81
The purpose of this study was to propose a time-series modeling and simulation (M&S) strategy for probabilistic cost-effective analysis in cancer chemotherapy using a Monte-Carlo method based on data available from the literature. The simulation included the cost for chemotherapy, for pharmaceutical care for adverse events (AEs) and other medical costs. As an application example, we describe the analysis for the comparison of four regimens, cisplatin plus irinotecan, carboplatin plus paclitaxel, cisplatin plus gemcitabine (GP), and cisplatin plus vinorelbine, for advanced non-small cell lung cancer. The factors, drug efficacy explained by overall survival or time to treatment failure, frequency and severity of AEs, utility value of AEs to determine QOL, the drugs’ and other medical costs in Japan, were included in the model. The simulation was performed and quality adjusted life years (QALY) and incremental cost-effectiveness ratios (ICER) were calculated. An index, percentage of superiority (%SUP) which is the rate of the increased cost vs. QALY-gained plots within the area of positive QALY-gained and also below some threshold values of the ICER, was calculated as functions of threshold values of the ICER. An M&S process was developed, and for the simulation example, the GP regimen was the most cost-effective, in case of threshold values of the ICER=$70000/year, the %SUP for the GP are more than 50%. We developed an M&S process for probabilistic cost-effective analysis, this method would be useful for decision-making in choosing a cancer chemotherapy regimen in terms of pharmacoeconomic.