Restricted mean survival time estimate using covariate adjusted pseudovalue regression to improve precision

<!DOCTYPE html>     <html class="no-js" lang="en">  <head> <title>Restricted mean survival time estimate using covariate adjusted pseudovalue regression to improve precision - NASA/ADS</title>  <link rel="apple-touch-icon" sizes="180x180" href="//styles/favicon/apple-touch-icon.png" /> <link rel="icon" type="image/png" sizes="32x32" href="//styles/favicon/favicon-32x32.png" /> <link rel="icon" type="image/png" sizes="16x16" href="//styles/favicon/favicon-16x16.png" /> <link rel="manifest" href="//styles/favicon/site.webmanifest" /> <link rel="mask-icon" href="//styles/favicon/safari-pinned-tab.svg" color="#5bbad5" /> <meta name="apple-mobile-web-app-title" content="NASA ADS" /> <meta name="application-name" content="NASA ADS" /> <meta name="msapplication-TileColor" content="#ffc40d" /> <meta name="theme-color" content="#ffffff" />  <link rel="stylesheet" href="/styles/css/styles.css"> <meta name="robots" content="noarchive"> <link rel="canonical" href="http://ui.adsabs.harvard.edu/abs/2022arXiv220804495L/abstract"/> <meta name="description" content="Covariate adjustment is desired by both practitioners and regulators of randomized clinical trials because it improves precision for estimating treatment effects. However, covariate adjustment presents a particular challenge in time-to-event analysis. We propose to apply covariate adjusted pseudovalue regression to estimate between-treatment difference in restricted mean survival times (RMST). Our proposed method incorporates a prognostic covariate to increase precision of treatment effect estimate, maintaining strict type I error control without introducing bias. In addition, the amount of increase in precision can be quantified and taken into account in sample size calculation at the study design stage. Consequently, our proposed method provides the ability to design smaller randomized studies at no expense to statistical power.">  <meta property="og:type" content="eprint"> <meta property="og:title" content="Restricted mean survival time estimate using covariate adjusted pseudovalue regression to improve precision"> <meta property="og:site_name" content="NASA/ADS"> <meta property="og:description" content="Covariate adjustment is desired by both practitioners and regulators of randomized clinical trials because it improves precision for estimating treatment effects. However, covariate adjustment presents a particular challenge in time-to-event analysis. We propose to apply covariate adjusted pseudovalue regression to estimate between-treatment difference in restricted mean survival times (RMST). Our proposed method incorporates a prognostic covariate to increase precision of treatment effect estimate, maintaining strict type I error control without introducing bias. In addition, the amount of increase in precision can be quantified and taken into account in sample size calculation at the study design stage. Consequently, our proposed method provides the ability to design smaller randomized studies at no expense to statistical power."> <meta property="og:url" content="https://ui.adsabs.harvard.edu/abs/2022arXiv220804495L/abstract"> <meta property="og:image" content="https://ui.adsabs.harvard.edu/styles/img/transparent_logo.svg"> <meta property="article:published_time" content="08/2022"> <meta property="article:author" content="Li, Yunfan"> <meta property="article:author" content="Ross, Jessica L."> <meta property="article:author" content="Smith, Aaron M."> <meta property="article:author" content="Miller, David P.">  <meta name="citation_journal_title" content="arXiv e-prints"> <meta name="citation_authors" content="Li, Yunfan;Ross, Jessica L.;Smith, Aaron M.;Miller, David P."> <meta name="citation_title" content="Restricted mean survival time estimate using covariate adjusted pseudovalue regression to improve precision"> <meta name="citation_date" content="08/2022"> <meta name="citation_firstpage" content="arXiv:2208.04495"> <meta name="citation_doi" content="10.48550/arXiv.2208.04495"> <meta name="citation_language" content="en"> <meta name="citation_keywords" content="Statistics - Methodology"> <meta name="citation_keywords" content="Statistics - Applications"> <meta name="citation_abstract_html_url" content="https://ui.adsabs.harvard.edu/abs/2022arXiv220804495L/abstract"> <meta name="citation_publication_date" content="08/2022"> <meta name="citation_arxiv_id" content="arXiv:2208.04495" /> <link title="schema(PRISM)" rel="schema.prism" href="http://prismstandard.org/namespaces/1.2/basic/" /> <meta name="prism.publicationDate" content="08/2022" /> <meta name="prism.publicationName" content="arXiv" /> <meta name="prism.startingPage" content="arXiv:2208.04495" /> <link title="schema(DC)" rel="schema.dc" href="http://purl.org/dc/elements/1.1/" /> <meta name="dc.identifier" content="doi:10.48550/arXiv.2208.04495" /> <meta name="dc.date" content="08/2022" /> <meta name="dc.source" content="arXiv" /> <meta name="dc.title" content="Restricted mean survival time estimate using covariate adjusted pseudovalue regression to improve precision" /> <meta name="dc.creator" content="Li, Yunfan"> <meta name="dc.creator" content="Ross, Jessica L."> <meta name="dc.creator" content="Smith, Aaron M."> <meta name="dc.creator" content="Miller, David P.">  <meta name="twitter:card" content="summary_large_image"/> <meta name="twitter:description" content="Covariate adjustment is desired by both practitioners and regulators of randomized clinical trials because it improves precision for estimating treatment effects. However, covariate adjustment presents a particular challenge in time-to-event analysis. We propose to apply covariate adjusted pseudovalue regression to estimate between-treatment difference in restricted mean survival times (RMST). Our proposed method incorporates a prognostic covariate to increase precision of treatment effect estimate, maintaining strict type I error control without introducing bias. In addition, the amount of increase in precision can be quantified and taken into account in sample size calculation at the study design stage. 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