{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hecker J"],"funding":["NHLBI NIH HHS","National Heart, Lung, and Blood Institute","NHGRI NIH HHS","Cure Alzheimer's Fund","National Human Genome Research Institute"],"pagination":["139-147"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7028451"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["44(2)"],"pubmed_abstract":["In the analysis of current life science datasets, we often encounter scenarios in which the application of asymptotic theory to hypothesis testing can be problematic. Besides improved asymptotic results, permutation/simulation-based tests are a general approach to address this issue. However, these randomized tests can impose a massive computational burden, for example, in scenarios in which large numbers of statistical tests are computed, and the specified significance level is very small. Stopping rules aim to assess significance with the smallest possible number of draws while controlling the probabilities of errors due to statistical uncertainty. In this communication, we derive a general stopping rule, QUICK-STOP, based on the sequential testing theory that is easy to implement, controls the error probabilities rigorously, and is nearly optimal in terms of expected draws. In a simulation study, we show that our approach outperforms current stopping approaches for general randomized tests by factor 10 and does not impose an additional computational burden. We illustrate our approach by applying our stopping rule to a single-variant analysis of a whole-genome sequencing study for lung function."],"journal":["Genetic epidemiology"],"pubmed_title":["A flexible and nearly optimal sequential testing approach to randomized testing: QUICK-STOP."],"pmcid":["PMC7028451"],"funding_grant_id":["HHSN268201500014C","R01HG008976","P01 HL120839","R01 HL120393","P01HL132825","R01 HL117626","U01HL089897","P01 HL132825","R01 HL089856","HHSN268201800001C","U01 HL089897","U01HL089856","U01 HL089856","R01 HG008976","P01HL120839"],"pubmed_authors":["Cho MH","Hecker J","Lange C","Coull B","Ruczinski I","Silverman EK"],"additional_accession":[]},"is_claimable":false,"name":"A flexible and nearly optimal sequential testing approach to randomized testing: QUICK-STOP.","description":"In the analysis of current life science datasets, we often encounter scenarios in which the application of asymptotic theory to hypothesis testing can be problematic. Besides improved asymptotic results, permutation/simulation-based tests are a general approach to address this issue. However, these randomized tests can impose a massive computational burden, for example, in scenarios in which large numbers of statistical tests are computed, and the specified significance level is very small. Stopping rules aim to assess significance with the smallest possible number of draws while controlling the probabilities of errors due to statistical uncertainty. In this communication, we derive a general stopping rule, QUICK-STOP, based on the sequential testing theory that is easy to implement, controls the error probabilities rigorously, and is nearly optimal in terms of expected draws. In a simulation study, we show that our approach outperforms current stopping approaches for general randomized tests by factor 10 and does not impose an additional computational burden. We illustrate our approach by applying our stopping rule to a single-variant analysis of a whole-genome sequencing study for lung function.","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Mar","modification":"2026-04-18T01:21:01.032Z","creation":"2021-03-03T08:09:12Z"},"accession":"S-EPMC7028451","cross_references":{"pubmed":["31713269"],"doi":["10.1002/gepi.22268"]}}