StochIKM: Automated High-Fidelity Reconstruction of Individual Patient Data from Published Kaplan-Meier Curves
Authors: Liangze Ke, Daniel Zelterman, Wei Wei, Guannan Gong
Organizations: Department of Biostatistics, School of Public Health, Yale University, New Haven, CT 06520, USA · Section of Medical Oncology, Yale School of Medicine and Yale Cancer Center, Yale University, New Haven, CT 06520, USA · Department of Medicine, Yale University School of Medicine and Yale Cancer Center, Yale University, New Haven, CT 06520, USA
Individual patient data are the gold standard for secondary analysis of time-to-event evidence, but they are rarely released, and reconstruction from a published Kaplan-Meier figure could help. Existing methods all claim high fidelity, each judged by whichever agreements it happens to achieve, and each returns a single dataset although the map from figure to data is not bijective. We define fidelity explicitly: a dataset is admissible when it reproduces the risk table exactly, follows the digitized curve to within the image resolution, and respects the printed median, interval and event count. We propose StochIKM, a stochastic algorithm that digitizes the figure with a vision-capable language model and uses the high-fidelity definition as constraints, returning an admissible set of individual patient datasets. A solution is guaranteed to exist whenever the figure has been read correctly, so a failure indicts either the published figure or its digitization. In simulation and in 79 arms from published oncology trials, StochIKM reproduces the published risk tables and medians more closely than LLM-improved IPDfromKM, a high-impact alternative.
Figures & tables
Figure 1: The StochIKM algorithm. A Flow chart of one run. B – G A toy example carried through the steps of A : one arm of 24 patients with a risk table at 0, 6, 12 and 18 months, a printed median with its confidence interval, and 14 printed events; C – F follow the second risk-table interval, I2 .
Figure 2: Reconstructions drawn over a published overall-survival figure of NCT00075764 22 : the K–M curves of the 50 admissible IPD of StochIKM (light blue, forming a band) and of the single IPD of LLM-improved IPDfromKM (orange, dashed), their numbers at risk under the published risk table, two enlarged regions (a, b), and the median survival with its 95% confidence interval (black, published). Time is in years; medians are in months.
StochIKM a
LLM-improved IPDfromKM b
Risk table reproduced exactly
97/97
44/97
Absolute error in the median survival (months)
Median
0.00
0.10
Mean
0.05
0.21
90th percentile
0.16
0.56
Maximum
0.91
1.32
Table 1: Reconstruction of 97 published arms by StochIKM and LLM-improved IPDfromKM.
Figure 3: Simulation with perfect digitization. Top row: mean over 1000 replicates of the difference between the reconstructed K–M curve and that of the simulated data (bias). Bottom row: its standard deviation (empirical SE). StochIKM (blue) is at zero throughout and its three lines coincide.
Copenhagen Research Centre for Biological and Precision Psychiatry, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark