{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T14:59:28Z","timestamp":1692975568385},"reference-count":5,"publisher":"Oxford University Press (OUP)","issue":"21","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Summary: In randomized clinical trials, identifying baseline genetic or genomic markers for predicting subgroup treatment effects is of rising interest. Outcome-dependent sampling is often employed for measuring markers. The R package TwoPhaseInd implements a number of efficient statistical methods we developed for estimating subgroup treatment effects and gene\u2013treatment interactions, exploiting the gene\u2013treatment independence dictated by randomization, including the case-only estimator, the maximum estimated likelihood estimator and the semiparametric maximum likelihood estimator for parameters in a logistic model. For rare failure events subject to censoring, we have proposed efficient augmented case-only designs, a variation of the case\u2013cohort design, to estimate genetic associations and subgroup treatment effects in a Cox regression model. The R package is computationally scalable to genome-wide studies, as illustrated by an example from Women\u2019s Health Initiative.<\/jats:p>\n               <jats:p>Availability and Implementation: The R package TwoPhaseInd is available from http:\/\/cran.r-project.org\/web\/packages .<\/jats:p>\n               <jats:p>Contact: \u00a0jdai@fredhutch.org<\/jats:p>","DOI":"10.1093\/bioinformatics\/btw391","type":"journal-article","created":{"date-parts":[[2016,7,5]],"date-time":"2016-07-05T01:59:34Z","timestamp":1467683974000},"page":"3348-3350","source":"Crossref","is-referenced-by-count":3,"title":["TwoPhaseInd: an R package for estimating gene\u2013treatment interactions and discovering predictive markers in randomized clinical trials"],"prefix":"10.1093","volume":"32","author":[{"given":"Xiaoyu","family":"Wang","sequence":"first","affiliation":[{"name":"1 Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA"}]},{"given":"James Y.","family":"Dai","sequence":"additional","affiliation":[{"name":"1 Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA"},{"name":"2 Department of Biostatistics, University of Washington, Seattle, WA, USA"}]}],"member":"286","published-online":{"date-parts":[[2016,7,4]]},"reference":[{"key":"2023020113522532100_btw391-B1","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1111\/j.1541-0420.2008.01046.x","article-title":"Semiparametric estimation exploiting covariate independence in two-phase randomized trials","volume":"65","author":"Dai","year":"2009","journal-title":"Biometrics"},{"key":"2023020113522532100_btw391-B2","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1093\/biomet\/ass044","article-title":"Two-stage testing procedures with independent filtering for genome-wide gene-environment interaction","volume":"99","author":"Dai","year":"2012","journal-title":"Biometrika"},{"key":"2023020113522532100_btw391-B3","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1093\/biostatistics\/kxt018","article-title":"Case-only methods for competing risks models with application to assessing differential vaccine efficacy by viral and host genetics","volume":"15","author":"Dai","year":"2014","journal-title":"Biostatistics"},{"key":"2023020113522532100_btw391-B4","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1111\/biom.12392","article-title":"Augmented case-only designs for randomized clinical trials with failure time endpoints","volume":"72","author":"Dai","year":"2016","journal-title":"Biometrics"},{"key":"2023020113522532100_btw391-B5","doi-asserted-by":"crossref","first-page":"1446","DOI":"10.1093\/jnci\/djp335","article-title":"Use of archived specimens in evaluation of prognostic and predictive biomarkers","volume":"101","author":"Simon","year":"2009","journal-title":"J. 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Cancer Inst"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/21\/3348\/49022595\/bioinformatics_32_21_3348.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/32\/21\/3348\/49022595\/bioinformatics_32_21_3348.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T23:54:14Z","timestamp":1675295654000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/32\/21\/3348\/2415087"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,7,4]]},"references-count":5,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2016,11,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btw391","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2016,11,1]]},"published":{"date-parts":[[2016,7,4]]}}}