{"id":106,"date":"2016-09-04T21:57:26","date_gmt":"2016-09-04T21:57:26","guid":{"rendered":"http:\/\/jsr.isrt.ac.bd\/?post_type=article&#038;p=106"},"modified":"2016-09-06T02:17:34","modified_gmt":"2016-09-06T02:17:34","slug":"marginal-models-for-binary-longitudinal-data-with-dropouts","status":"publish","type":"article","link":"http:\/\/jsr.isrt.ac.bd\/article\/marginal-models-for-binary-longitudinal-data-with-dropouts\/","title":{"rendered":"Marginal models for binary longitudinal data with dropouts"},"content":{"rendered":"<p>In this paper, we propose and explore a set of weighted generalized estimating equations for\u00a0fitting regression models to longitudinal binary responses when there are dropouts. Under\u00a0a given missing data mechanism, the proposed method provides unbiased estimators of\u00a0the regression parameters and the association parameters. Simulations were carried out to\u00a0study the robustness properties of the proposed method under both correctly specified and\u00a0misspecified correlation structures. The method is also illustrated in an analysis of some\u00a0actual incomplete longitudinal data on cigarette smoking trends, which were used to study\u00a0coronary artery development in young adults.<\/p>\n<p><a href=\"http:\/\/jsr.isrt.ac.bd\/wp-content\/uploads\/47n1_4.pdf\">Fulltext<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we propose and explore a set of weighted generalized estimating equations for\u00a0fitting regression models to longitudinal binary responses when there are dropouts. Under\u00a0a given missing data mechanism, the proposed method provides unbiased estimators of\u00a0the regression parameters and the association parameters. Simulations were carried out to\u00a0study the robustness properties of the proposed method [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"menu_order":0,"comment_status":"open","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":[2],"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>Marginal models for binary longitudinal data with dropouts - 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\/marginal-models-for-binary-longitudinal-data-with-dropouts\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Marginal models for binary longitudinal data with dropouts - JSR\" \/>\n<meta property=\"og:description\" content=\"In this paper, we propose and explore a set of weighted generalized estimating equations for\u00a0fitting regression models to longitudinal binary responses when there are dropouts. Under\u00a0a given missing data mechanism, the proposed method provides unbiased estimators of\u00a0the regression parameters and the association parameters. Simulations were carried out to\u00a0study the robustness properties of the proposed method [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/jsr.isrt.ac.bd\/article\/marginal-models-for-binary-longitudinal-data-with-dropouts\/\" \/>\n<meta property=\"og:site_name\" content=\"JSR\" \/>\n<meta property=\"article:modified_time\" content=\"2016-09-06T02:17:34+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/jsr.isrt.ac.bd\/article\/marginal-models-for-binary-longitudinal-data-with-dropouts\/\",\"url\":\"https:\/\/jsr.isrt.ac.bd\/article\/marginal-models-for-binary-longitudinal-data-with-dropouts\/\",\"name\":\"Marginal models for binary longitudinal data with dropouts - JSR\",\"isPartOf\":{\"@id\":\"https:\/\/jsr.isrt.ac.bd\/#website\"},\"datePublished\":\"2016-09-04T21:57:26+00:00\",\"dateModified\":\"2016-09-06T02:17:34+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/jsr.isrt.ac.bd\/article\/marginal-models-for-binary-longitudinal-data-with-dropouts\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/jsr.isrt.ac.bd\/article\/marginal-models-for-binary-longitudinal-data-with-dropouts\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/jsr.isrt.ac.bd\/article\/marginal-models-for-binary-longitudinal-data-with-dropouts\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/jsr.isrt.ac.bd\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Articles\",\"item\":\"https:\/\/jsr.isrt.ac.bd\/article\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Marginal models for binary longitudinal data with dropouts\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/jsr.isrt.ac.bd\/#website\",\"url\":\"https:\/\/jsr.isrt.ac.bd\/\",\"name\":\"JSR\",\"description\":\"Journal of Statistical Research\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/jsr.isrt.ac.bd\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Marginal models for binary longitudinal data with dropouts - JSR","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/jsr.isrt.ac.bd\/article\/marginal-models-for-binary-longitudinal-data-with-dropouts\/","og_locale":"en_US","og_type":"article","og_title":"Marginal models for binary longitudinal data with dropouts - JSR","og_description":"In this paper, we propose and explore a set of weighted generalized estimating equations for\u00a0fitting regression models to longitudinal binary responses when there are dropouts. Under\u00a0a given missing data mechanism, the proposed method provides unbiased estimators of\u00a0the regression parameters and the association parameters. 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