Bioinformatics. 2021 Jul 13:btab495. doi: 10.1093/bioinformatics/btab495. Online ahead of print.
ABSTRACT
MOTIVATION: Log rank test is a widely used test that serves to assess the statistical significance of observed differences in survival, when comparing two or more groups. The log rank test is based on several assumptions that support the validity of the calculations. It is naturally assumed, implicitly, that no errors occur in the labeling of the samples. That is – that the mapping between samples and groups is perfectly correct. In this work we investigate how test results may be affected when considering some errors in the original labeling.
RESULTS: We introduce and define the uncertainty that arises from labeling errors in log rank test. In order to deal with this uncertainty, we develop a novel algorithm for efficiently calculating a stability interval around the original log rank p-value and prove its correctness. We demonstrate our algorithm on several datasets.
AVAILABILITY: We provide a Python implementation, called LoRSI, for calculating the stability interval using our algorithm. https://github.com/YakhiniGroup/LoRSI.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
PMID:34255820 | DOI:10.1093/bioinformatics/btab495
