{"id":3006,"date":"2018-09-18T13:21:24","date_gmt":"2018-09-18T17:21:24","guid":{"rendered":"https:\/\/www.med.unc.edu\/bigs2\/?page_id=3006"},"modified":"2020-10-13T19:18:48","modified_gmt":"2020-10-13T23:18:48","slug":"prm-mip","status":"publish","type":"page","link":"https:\/\/www.med.unc.edu\/bigs2\/prm-mip\/","title":{"rendered":"PRM-MIP: Projection Regression Models for Multivariate Imaging Phenotype"},"content":{"rendered":"<div id=\"content1\">\n<div id=\"main\">\n<div id=\"right\">\n<div id=\"right_text\">\n<div class=\"box\">\n<p>This paper presents a projection regression model (PRM) to assess the relationship between a multivariate phenotype and a set of covariates, such as a genetic marker, age and gender.\u00a0In the existing literature, a standard statistical approach to this problem is to fit a multivariate linear model to the multivariate phenotype and then use Hotelling&#8217;s $T^2$ to test hypotheses of interest. An alternative approach is to fit a simple linear model and test hypotheses for\u00a0each individual phenotype and then correct for multiplicity. However, even when the dimension of the multivariate phenotype is relatively small, say 5, such standard approaches can suffer from the issue of low statistical power in detecting the association between the multivariate phenotype and the covariates. The PRM generalizes<br \/>\na statistical method based on the principal component of heritability for association analysis in genetic studies of complex multivariate phenotypes.\u00a0The key components of the PRM include an estimation procedure for extracting several principal directions of multivariate phenotypes relating to covariates and a test procedure based on wild-bootstrap method for testing for the association between the weighted multivariate phenotype and explanatory variables. Simulation studies and an imaging genetic dataset are used to examine the finite sample performance of the PRM.<\/p>\n<p><strong><a href=\"https:\/\/www.med.unc.edu\/bigs2\/wp-content\/uploads\/sites\/822\/2018\/09\/PRM_MIP.zip\">Download<\/a><\/strong><\/p>\n<p>&nbsp;<\/p>\n<p><strong>Citation<\/strong>: Lin JA, Zhu H, Knickmeyer R, Styner M, Gilmore J, Ibrahim JG. Projection regression models for multivariate imaging phenotype. <em>Genet Epidemiol<\/em>. 2012.<\/p>\n<p>&nbsp;<\/p>\n<\/div>\n<\/div>\n<\/div>\n<div id=\"footer\">\n<p>&nbsp;<\/p>\n<\/div>\n<\/div>\n<p><!-- footer ends--><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a projection regression model (PRM) to assess the relationship between a multivariate phenotype and a set of covariates, such as a genetic marker, age and gender.\u00a0In the existing literature, a standard statistical approach to this problem is to fit a multivariate linear model to the multivariate phenotype and then use Hotelling&#8217;s $T^2$ &hellip; <a href=\"https:\/\/www.med.unc.edu\/bigs2\/prm-mip\/\" aria-label=\"Read more about PRM-MIP: Projection Regression Models for Multivariate Imaging Phenotype\">Read more<\/a><\/p>\n","protected":false},"author":1503,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-3006","page","type-page","status-publish","hentry","odd"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>PRM-MIP: Projection Regression Models for Multivariate Imaging Phenotype - BIG-S2<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PRM-MIP: Projection Regression Models for Multivariate Imaging Phenotype - BIG-S2\" \/>\n<meta property=\"og:description\" content=\"This paper presents a projection regression model (PRM) to assess the relationship between a multivariate phenotype and a set of covariates, such as a genetic marker, age and gender.\u00a0In the existing literature, a standard statistical approach to this problem is to fit a multivariate linear model to the multivariate phenotype and then use Hotelling&#8217;s $T^2$ &hellip; 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