Survival Probability Extraction and Performance Comparison of Kaplan-Meier Curves
- VernacularTitle:Kaplan-Meier曲线生存概率提取方法与结果比较
- Author:
Lifeng MU
1
;
Longying MAO
;
Yun MAO
;
Xin CHEN
;
Long CHEN
;
Ming YANG
Author Information
1. 川北医学院附属医院药学部 四川 南充 637000;川北医学院药学院 四川 南充 637000
- Publication Type:Journal Article
- Keywords:
Kaplan-Meier curve;
survival probability extraction;
survival analysis
- From:
Chinese Health Economics
2025;44(7):36-39
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To plot Kaplan-Meier curves using simulated survival data and compare the characteristics of different survival probability extraction methods for Kaplan-Meier curves and their performance across various scenarios.Methods:Survival datasets were simulated using R-4.4.2 with parameters including sample sizes,censoring marker,and curve numbers.GetData Graph Digitizer,IPDfromKM getpoints,SurvdigitizeR survival_digitize were evaluated.A JavaScript script was developed to extract Kaplan-Meier curve.Root Mean Square Error(RMSE)was calculated to quantify deviations between digitized and true survival probabilities.Results:The JavaScript script method demonstrated the smallest RMSE across all simulated scenarios(RMSE=1.015×10-4),significantly outperforming the other three methods(P＜0.05),with statistically significant differences observed among methods(P＜0.05).Conclusion:For vector format illustrations,JavaScript scripts enable accurate and robust reverse engineering of Kaplan-Meier curves;for bitmaps,the GetData Graph Digitizer and SurvdigitizeR survival_digitize methods yield more accurate results,and the SurvdigitizeR survival_digitize method is the most efficient.Future research should focus on integrating intelligent algorithms for enhanced robustness and precision in survival data reconstruction.
