{"id":673,"date":"2017-09-28T08:48:57","date_gmt":"2017-09-28T08:48:57","guid":{"rendered":"http:\/\/jsr.isrt.ac.bd\/?post_type=article&#038;p=673"},"modified":"2017-09-28T08:49:07","modified_gmt":"2017-09-28T08:49:07","slug":"approximate-method-frailty-model-presence-immune-proportion","status":"publish","type":"article","link":"http:\/\/jsr.isrt.ac.bd\/article\/approximate-method-frailty-model-presence-immune-proportion\/","title":{"rendered":"An approximate method for a frailty model in the presence of an immune proportion"},"content":{"rendered":"<p>This article considers an extension of the existing survival model with an immune<br \/>\nproportion known as a latent data (LD) model. Random effects are introduced<br \/>\nin this LD model. A generalized linear mixed model using a penalized quasi<br \/>\nlikelihood approach for the parameter estimates is proposed. The model enables<br \/>\nthe prediction of the random effect and retains the proportional hazard property<br \/>\nof the LD model. Application of the method is carried out on two real data<br \/>\nsets. A simulation study is conducted to evaluate the model\u2019s performance. Two<br \/>\ndifferent types of censoring are considered. The results show that the estimates<br \/>\nhave relatively small bias in all cases and the method works equally well in both<br \/>\nthe random and fixed censoring cases.<\/p>\n<p><a href=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/uploads\/39n2_9.pdf\">Fulltext<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This article considers an extension of the existing survival model with an immune proportion known as a latent data (LD) model. Random effects are introduced in this LD model. A generalized linear mixed model using a penalized quasi likelihood approach for the parameter estimates is proposed. The model enables the prediction of the random effect [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","format":"standard","meta":{"_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"issuem_issue":[21],"issuem_issue_categories":[],"issuem_issue_tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>An approximate method for a frailty model in the presence of an immune proportion - JSR<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/jsr.isrt.ac.bd\/article\/approximate-method-frailty-model-presence-immune-proportion\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"An approximate method for a frailty model in the presence of an immune proportion - JSR\" \/>\n<meta property=\"og:description\" content=\"This article considers an extension of the existing survival model with an immune proportion known as a latent data (LD) model. Random effects are introduced in this LD model. A generalized linear mixed model using a penalized quasi likelihood approach for the parameter estimates is proposed. 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Random effects are introduced in this LD model. A generalized linear mixed model using a penalized quasi likelihood approach for the parameter estimates is proposed. 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