TECHNOLOGY AND CODE article
Front. Pharmacol.
Sec. Drugs Outcomes Research and Policies
Volume 16 - 2025 | doi: 10.3389/fphar.2025.1554405
OncoPSM: an interactive tool for cost-effectiveness analysis using partitioned survival models in oncology trial
Provisionally accepted
- 1Shantou University Medical College, Shantou, Guangdong Province, China
- 2Department of Cardiothoracic Surgery, Shantou Central Hospital, Shantou, Guangdong Province, China
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Introduction: cost-effectiveness analysis (CEA) serves as a critical tool to evaluate the economic sustainability of new treatments. However, many CEA tools are not specifically tailored to address the intricate cost composition resulting from the complex treatment regimens in oncology trials.We extracted data from Kaplan-Meier (KM) curves, reconstructed individual patient data (IPD) using an iterative KM algorithm, and fitted parametric survival functions to the IPD data. Based on these functions, we constructed Partitioned Survival Model (PSM), calculated the probability of each survival state per cycle, and combined these with utility values to compute the effect per cycle and the incremental effect for the experimental group. We employed a treatment-cycle-specific cost analysis, simulating cost uncertainty through gamma distribution. Using the PSM, we calculated the state-weighted cost, applied a discount rate, determined the incremental cost for the experimental group, and calculated the Incremental Cost-Effectiveness Ratio (ICER) .The OncoPSM application is available at http://sw2-primary1.xiyoucloud.pro:13471/oncoPSM/. Validation with real-world data from the CHOICE-01 trial showed that OncoPSM accurately reconstructed IPD from KM curve, with RMSE below 0.004 for all curves. Log-rank p-values for the experimental and control groups (PFS: <0.001; OS: 0.010) closely matched the original article (PFS: <0.001; OS: 0.010). Hazard Ratios (HR) from reconstructed IPD data (PFS
Keywords: cost-effectiveness analysis, incremental cost-effectiveness ratio, partitioned survival model, oncology, lung cancer
Received: 25 Feb 2025; Accepted: 29 Jul 2025.
Copyright: © 2025 Qiu, Chen, Wang, Zheng and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ruixiong Li, Department of Cardiothoracic Surgery, Shantou Central Hospital, Shantou, Guangdong Province, China
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