{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T10:42:46Z","timestamp":1719225766780},"reference-count":0,"publisher":"Index Copernicus","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bio-Algorithms and Med-Systems"],"published-print":{"date-parts":[[2013,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Causal inference in survival analysis has been centered on treatment effect assessment with adjustment of covariates. The direct adjustment method is usually employed to find the survival function of a treatment. A Cox model that stratifies the cumulative hazard by treatment is an ideal choice for performing direct adjustment because the treatment effects are allowed to vary over time. A SAS macro was developed to implement comparison of direct adjusted survivals between treatments at a selected time point. The restricted mean survival time can be derived from a direct adjusted survival function. This statistic summarizes the survival outcome of a treatment. Comparison of restricted means provides assessment of treatment effect over a time interval. The first aim of this article was to provide an overview of the restricted mean survival time. The second aim was to introduce a SAS macro that computes the restricted mean survival times from direct adjusted survivals based on a stratified Cox model. Data preparation and macro invocation are illustrated in an analysis of survival data involving three types of stem cell transplants.<\/jats:p>","DOI":"10.1515\/bams-2013-0101","type":"journal-article","created":{"date-parts":[[2013,11,28]],"date-time":"2013-11-28T20:13:42Z","timestamp":1385669622000},"page":"183-189","source":"Crossref","is-referenced-by-count":5,"title":["Comparison of restricted mean survival times between treatments based on a stratified Cox model"],"prefix":"10.5604","volume":"9","author":[{"given":"Xu","family":"Zhang","sequence":"first","affiliation":[{"name":"Center of Biostatistics and Bioinformatics, Cancer Institute, University of Mississippi Medical Center, 2500 North State Street, Jackson, MI 39216, USA"}]}],"member":"3689","published-online":{"date-parts":[[2013,11,29]]},"container-title":["bams"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/bams-2013-0101\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyter.com\/document\/doi\/10.1515\/bams-2013-0101\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T10:09:09Z","timestamp":1719223749000},"score":1,"resource":{"primary":{"URL":"https:\/\/bamsjournal.com\/resources\/html\/article\/details?id=616694"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,11,29]]},"references-count":0,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2013,11,29]]},"published-print":{"date-parts":[[2013,12,1]]}},"alternative-id":["10.1515\/bams-2013-0101"],"URL":"https:\/\/doi.org\/10.1515\/bams-2013-0101","relation":{},"ISSN":["1896-530X","1895-9091"],"issn-type":[{"value":"1896-530X","type":"electronic"},{"value":"1895-9091","type":"print"}],"subject":[],"published":{"date-parts":[[2013,11,29]]}}}