Abstract 3566: OncoPSM as a tool for cost-effectiveness analysis in oncology trial using Kaplan Meier curve data
Authors: Baohui Han, Feilong Zhao, Ding Zhang
Journal: Cancer Research (2025)

Abstract Introduction: Cost-effectiveness analysis (CEA) is a key tool for evaluating the economic viability of new treatments compared to current standards of care. However, many CEA methodologies are not specifically designed to address the intricate cost structures in oncology trials. Methods: We employed a public image recognition tool to extract data from Kaplan-Meier (KM) curves published in clinical trials. Using an iterative algorithm based on the iKM method, we reconstructed individual patient data (IPD). A partitioned survival models (PSM) were then fitted to the IPD data. Under this model, we estimated the probability of each survival state for each model-cycle and combine these probabilities with utility values to calculate both the effect per model-cycle and the incremental effect for the experimental group. For cost analysis, we performed a treatment-cycle-specific cost evaluation, accounting for cost uncertainty through a gamma distribution. By setting model-cycle equals to the treatment-cycle, we could calculate the state-weighted cost, applied a discount rate, and determined the incremental cost for the experimental group. The Incremental Cost-Effectiveness Ratio (ICER) was then calculated based on the incremental effect and incremental cost. Results: The OncoPSM application, available at (https://zhaofeilong.shinyapps.io/app_oncopsa/), was validated with real-world data from the CHOICE-01 trial. OncoSPM accurately reconstructed IPD from KM curve data, with root mean square error (RMSE) values below 0.004 across all curves. Log-rank p-values for the experimental and control groups (PFS: &amp;lt;0.0001; OS: 0.0104) closely aligned with those reported in the original publication (PFS: &amp;lt;0.0001; OS: 0.0108). Hazard ratios (HR) from the reconstructed IPD data (PFS: 0.504, 95%CI: 0.4-0.63, OS: 0.731, 95%CI: 0.57-0.93) were consistent with the original study (PFS: 0.49, 95%CI: 0.39-0.61, OS: 0.73, 95%CI: 0.57-0.93). The log-logistic model provided the best fit for both PFS and OS curves, based on the Akaike Information Criterion (AIC). Extrapolating the survival curve to a 10-year horizon, we created the PSM, deriving the average state probabilities per treatment-cycle to calculate state-weighted costs. The incremental cost for the experimental group was ¥42, 291, with an incremental quality-adjusted life year (QALY) of 0.35, yielding an ICER of 122, 319 ¥/QALY, which is well below the willingness-to-pay (WTP) threshold of 268, 200 ¥/QALY. Uncertainty analysis showed a 99.6% probability that the experimental group was cost-effective at the WTP threshold. Conclusions: OncoPSM offers an accurate and user-friendly treatment-cycle-based cost analysis tool that addresses the complexities of costs in oncology research. By integrating and visualizing the entire CEA process, OncoPSM supports decision-makers in making well-informed choices based on both statistical and intuitive insights. Citation Format: Baohui Han, Feilong Zhao, Ding Zhang. OncoPSM as a tool for cost-effectiveness analysis in oncology trial using Kaplan Meier curve data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3566.