ArticleFrontiers in pharmacology2025
OncoPSM: an interactive tool for cost-effectiveness analysis using partitioned survival models in oncology trial.
Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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Abstract
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. Methods: 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). Results: 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: 0.504 [0.4-0.63], OS: 0.731 [0.57-0.93]) were consistent with the original paper (PFS: 0.49 [0.39-0.61], OS: 0.73 [0.57-0.93]). The Log-logistic model provided the optimal fit for both PFS and OS curves according to the Akaike Information Criterion (AIC). Extrapolating the survival to a 10-year horizon, we created the PSM, derived the average state probability per cycle, and calculated state-weighted costs. The incremental cost for the experimental group was ¥42,068, with incremental quality-adjusted life years (QALYs) of 0.35, resulting in an ICER of 121,402, significantly below the willing-to-pay (WTP) threshold of 268,200 RMB/QALY. Uncertainty analysis showed a 99.7% probability of the experimental group being cost-effective. Conclusion: OncoPSM provides convenient treatment-cycle-based cost analysis, addressing the complexities of treatment costs in oncology research. By visualizing the entire CEA process, OncoPSM enables decision-makers to make informed decisions based on both statistical and intuitive assessments.
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