RESOLVE-IPD: High-Fidelity IPD Reconstruction and Uncertainty-Aware Subgroup Meta-Analysis

Thomas M. Menino Convention & Exhibition Center 

Individual patient data (IPD) are essential for oncology evidence synthesis but are rarely available, motivating reconstruction from published KM curves. Existing methods are limited by digitization error, unrealistic censoring assumptions, and inability to recover subgroup IPD from aggregate summaries. We propose RESOLVE-IPD, a unified framework for high-fidelity IPD reconstruction and uncertainty-aware subgroup recovery. The reconstruction engine combines VEC-KM, which extracts precise KM coordinates and censoring marks from vectorized figures, and CEN-KM, which resolves overlapping censor symbols without assuming uniform censoring. The subgroup module, MAPLE, identifies an ensemble of data-compatible labelings that match reported statistics and enables meta-analysis with explicit propagation of subgroup uncertainty. Across four trials in advanced esophageal squamous cell carcinoma focused on the PD-L1–low population, reconstructed IPD closely matched published KM curves and summary statistics, while MAPLE recovered plausible subgroup assignments. Ensemble meta-analysis demonstrated a survival benefit of immunotherapy over chemotherapy, most pronounced between 6 and 12 months.

Survival Analysis

Meta Analysis

IPD

Optimization

Oncology 

Biopharmaceutical Section