KMSubtraction: reconstruction of unreported subgroup survival data utilizing published Kaplan-Meier survival curves

• Published in: BMC medical research methodology Vol. 22; no. 1; p. 93
• Main Authors: Zhao, Joseph J, Syn, Nicholas L, Tan, Benjamin Kye Jyn, Yap, Dominic Wei Ting, Teo, Chong Boon, Chan, Yiong Huak, Sundar, Raghav
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 03.04.2022; BioMed Central; BMC
• Subjects: Algorithms; Biomarkers; Cancer survivors; Care and treatment; Gene expression; Health aspects; Humans; Kaplan-Meier Estimate; Lymphatic system; Medical research; Monte Carlo Method; Patients; Planning; Proportional Hazards Models; Reproducibility of Results; Research methodology; Simulation; Variance analysis; Singapore
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-022-01567-z

Data from certain subgroups of clinical interest may not be presented in primary manuscripts or conference abstract presentations. In an effort to enable secondary data analyses, we propose a workflow to retrieve unreported subgroup survival data from published Kaplan-Meier (KM) plots. We developed KMSubtraction, an R-package that retrieves patients from unreported subgroups by matching participants on KM plots of the overall cohort to participants on KM plots of a known subgroup with follow-up time. By excluding matched patients, the opposing unreported subgroup may be retrieved. Reproducibility and limits of error of the KMSubtraction workflow were assessed by comparing unmatched patients against the original survival data of subgroups from published datasets and simulations. Monte Carlo simulations were utilized to evaluate the limits of error of KMSubtraction. The validation exercise found no material systematic error and demonstrates the robustness of KMSubtraction in deriving unreported subgroup survival data. Limits of error were small and negligible on marginal Cox proportional hazard models comparing reconstructed and original survival data of unreported subgroups. Extensive Monte Carlo simulations demonstrate that datasets with high reported subgroup proportion (r = 0.467, p < 0.001), small dataset size (r = - 0.374, p < 0.001) and high proportion of missing data in the unreported subgroup (r = 0.553, p < 0.001) were associated with uncertainty are likely to yield high limits of error with KMSubtraction. KMSubtraction demonstrates robustness in deriving survival data from unreported subgroups. The limits of error of KMSubtraction derived from converged Monte Carlo simulations may guide the interpretation of reconstructed survival data of unreported subgroups.