A nonparametric statistical method for two crossing survival curves

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...

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Vydáno v:Communications in statistics. Simulation and computation Ročník 51; číslo 9; s. 5041 - 5050
Hlavní autoři: Huang, Xinghui, Lyu, Jingjing, Hou, Yawen, Chen, Zheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Philadelphia Taylor & Francis 27.09.2022
Taylor & Francis Ltd
Témata:
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
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2020.1753075