ArticlePLoS computational biology2015
Accurate computation of survival statistics in genome-wide studies.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- The landscape of NNature genetics · 2025Article
- Systematic Modeling of Risk-Associated Copy Number Alterations in Cancer.International journal of molecular sciences · 2024Article
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- The Impact of Mutational Hotspots on Cancer Survival.Cancers · 2024Article
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- The role of ERBB4 mutations in the prognosis of advanced non-small cell lung cancer treated with immune checkpoint inhibitors.Molecular medicine (Cambridge, Mass.) · 2021Article
- Molecular classification and diagnostics of upper urinary tract urothelial carcinoma.Cancer cell · 2021Article
- Patients WithJournal of the American Heart Association · 2021Article
- MONET: Multi-omic module discovery by omic selection.PLoS computational biology · 2020Article
- Network-based analysis of prostate cancer cell lines reveals novel marker gene candidates associated with radioresistance and patient relapse.PLoS computational biology · 2019Article
- Genomic Drivers of Poor Prognosis and Enzalutamide Resistance in Metastatic Castration-resistant Prostate Cancer.European urology · 2019Article
- NEMO: cancer subtyping by integration of partial multi-omic data.Bioinformatics (Oxford, England) · 2019Article
- NoMAS: A Computational Approach to Find Mutated Subnetworks Associated With Survival in Genome-Wide Cancer Studies.Frontiers in genetics · 2019Article
- Mutations in an Innate Immunity Pathway Are Associated with Poor Overall Survival Outcomes and Hypoxic Signaling in Cancer.Cell reports · 2018Article
- Multi-omic and multi-view clustering algorithms: review and cancer benchmark.Nucleic acids research · 2018Review
- Identification of outcome-related driver mutations in cancer using conditional co-occurrence distributions.Scientific reports · 2017Article
- Computational Methods for Characterizing Cancer Mutational Heterogeneity.Frontiers in genetics · 2017Review
- Inherited Disease Genetics Improves the Identification of Cancer-Associated Genes.PLoS genetics · 2016Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
A key challenge in genomics is to identify genetic variants that distinguish patients with different survival time following diagnosis or treatment. While the log-rank test is widely used for this purpose, nearly all implementations of the log-rank test rely on an asymptotic approximation that is not appropriate in many genomics applications. This is because: the two populations determined by a genetic variant may have very different sizes; and the evaluation of many possible variants demands highly accurate computation of very small p-values. We demonstrate this problem for cancer genomics data where the standard log-rank test leads to many false positive associations between somatic mutations and survival time. We develop and analyze a novel algorithm, Exact Log-rank Test (ExaLT), that accurately computes the p-value of the log-rank statistic under an exact distribution that is appropriate for any size populations. We demonstrate the advantages of ExaLT on data from published cancer genomics studies, finding significant differences from the reported p-values. We analyze somatic mutations in six cancer types from The Cancer Genome Atlas (TCGA), finding mutations with known association to survival as well as several novel associations. In contrast, standard implementations of the log-rank test report dozens-hundreds of likely false positive associations as more significant than these known associations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.