A nonparametric statistical method for two crossing survival curves

• Published in: Communications in statistics. Simulation and computation Vol. 51; no. 9; pp. 5041 - 5050
• Main Authors: Huang, Xinghui, Lyu, Jingjing, Hou, Yawen, Chen, Zheng
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 27.09.2022; Taylor & Francis Ltd
• Subjects: Area between two survival curves; Crossing survival curves; Evaluation; Hazards; Nonparametric statistics; Permutations; Rank tests; Reliability analysis; Robustness; Statistical inference; Statistical methods; Survival; Survival analysis; Treatment benefit
• ISSN: 0361-0918; 1532-4141
• DOI: 10.1080/03610918.2020.1753075

In comparative research on time-to-event data for two groups, when two survival curves cross each other, it may be difficult to use the log-rank test and hazard ratio (HR) to properly assess the treatment benefit. Our aim was to identify a method for evaluating the treatment benefits for two groups in the above situation. We quantified treatment benefits based on an intuitive measure called the area between two survival curves (ABS), which is a robust measure of treatment benefits in clinical trials regardless of whether the proportional hazards assumption is violated or two survival curves cross each other. Additionally, we propose a permutation test based on the ABS, and we evaluate the effectiveness and reliability of this test with simulated data. The ABS permutation test is a robust statistical inference method with an acceptable type I error rate and superior power to detect differences in treatment effects, especially when the proportional hazards assumption is violated. The ABS can be used to intuitively quantify treatment differences over time and provide reliable conclusions in complicated situations, such as crossing survival curves.