{"id":9652,"date":"2025-12-09T11:00:15","date_gmt":"2025-12-09T11:00:15","guid":{"rendered":"https:\/\/kibu.ac.ke\/sgs\/?p=9652"},"modified":"2025-12-09T11:02:03","modified_gmt":"2025-12-09T11:02:03","slug":"comparative-performance-analysis-of-bayesian-hierarchical-models-versus-classical-statistical-approaches-in-predicting-breast-cancer-treatment-outcomes-evidence-from-kenyan-healthcare-settings","status":"publish","type":"post","link":"https:\/\/kibu.ac.ke\/sgs\/comparative-performance-analysis-of-bayesian-hierarchical-models-versus-classical-statistical-approaches-in-predicting-breast-cancer-treatment-outcomes-evidence-from-kenyan-healthcare-settings\/","title":{"rendered":"Comparative Performance Analysis of Bayesian Hierarchical Models Versus Classical Statistical Approaches in Predicting Breast Cancer Treatment Outcomes: Evidence from Kenyan Healthcare Settings"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"9652\" class=\"elementor elementor-9652\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-19202d81 elementor-section-full_width elementor-section-height-min-height elementor-section-items-stretch elementor-section-height-default\" data-id=\"19202d81\" data-element_type=\"section\" id=\"ourgoal\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6d200902\" data-id=\"6d200902\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-97123dc elementor-widget elementor-widget-theme-post-title elementor-page-title elementor-widget-heading\" data-id=\"97123dc\" data-element_type=\"widget\" data-widget_type=\"theme-post-title.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Comparative Performance Analysis of Bayesian Hierarchical Models Versus Classical Statistical Approaches in Predicting Breast Cancer Treatment Outcomes: Evidence from Kenyan Healthcare Settings<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1a7a5e97 elementor-section-height-min-height elementor-section-boxed elementor-section-height-default elementor-section-items-middle\" data-id=\"1a7a5e97\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5b6c0302\" data-id=\"5b6c0302\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-59c89fba elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"59c89fba\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-725010a4 elementor-invisible\" data-id=\"725010a4\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;,&quot;animation_mobile&quot;:&quot;none&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7b84536a elementor-widget__width-auto elementor-widget elementor-widget-heading\" data-id=\"7b84536a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">2025<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-6f8f9e9e elementor-invisible\" data-id=\"6f8f9e9e\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;,&quot;animation_mobile&quot;:&quot;none&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-78bafe54 elementor-widget elementor-widget-heading\" data-id=\"78bafe54\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Authors<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-79b5080f elementor-invisible elementor-widget elementor-widget-text-editor\" data-id=\"79b5080f\" data-element_type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeIn&quot;}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Muhati Nelson Lwoyelo<\/p><p>Richard Simwa<\/p><p>Vincent Marani<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-3acfc5ae elementor-invisible\" data-id=\"3acfc5ae\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;,&quot;animation_mobile&quot;:&quot;none&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-59875b71 elementor-tablet-align-left elementor-invisible elementor-widget elementor-widget-button\" data-id=\"59875b71\" data-element_type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeInLeft&quot;}\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-shrink\" href=\"https:\/\/www.irejournals.com\/paper-details\/1709508\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">VIEW ON PUBLISHER SITE<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<div class=\"elementor-element elementor-element-16306c04 elementor-widget elementor-widget-heading\" data-id=\"16306c04\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6c24dfc1 elementor-invisible elementor-widget elementor-widget-text-editor\" data-id=\"6c24dfc1\" data-element_type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeIn&quot;}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Current breast cancer treatment prediction models inadequately quantify uncertainty and fail to account for institutional clustering effects, particularly in resource-constrained healthcare settings. This study compared the performance of Bayesian hierarchical models against classical frequentist approaches for predicting pathological complete response (pCR) in breast cancer patients. We analyzed data from 5,400 breast cancer patients across 12 Kenyan treatment centers. Three progressively complex models were developed: single-level logistic regression (M0), Bayesian empty hierarchical model (M1), and Bayesian hierarchical model with clinical covariates (M2). Performance comparison utilized multiple metrics including Area Under the Curve (AUC), Brier Score, calibration measures, and information criteria. The Bayesian hierarchical model demonstrated superior performance with AUC = 0.837 compared to classical approaches (AUC = 0.752). Bayesian methods showed consistent 2-8 unit improvements in information criteria across all model complexity levels. The hierarchical structure captured 26.5% of outcome variation attributable to institutional clustering (ICC = 0.265), which classical models failed to address. Uncertainty quantification through credible intervals provided clinically meaningful prediction confidence assessment. Bayesian hierarchical approaches significantly outperform classical statistical methods in breast cancer treatment outcome prediction, particularly in settings with institutional clustering. The explicit uncertainty quantification and superior discrimination make Bayesian methods more suitable for clinical decision-making in resource-constrained environments.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>2025 Authors Muhati Nelson Lwoyelo Richard Simwa Vincent Marani VIEW ON PUBLISHER SITE Abstract Current breast cancer treatment prediction models inadequately quantify uncertainty and fail to account for institutional clustering effects, particularly in resource-constrained healthcare settings. This study compared the performance of Bayesian hierarchical models against classical frequentist approaches for predicting pathological complete response (pCR) [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-9652","post","type-post","status-publish","format-standard","hentry","category-publications"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Comparative Performance Analysis of Bayesian Hierarchical Models Versus Classical Statistical Approaches in Predicting Breast Cancer Treatment Outcomes: Evidence from Kenyan Healthcare Settings - School of Graduate Studies<\/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:\/\/kibu.ac.ke\/sgs\/comparative-performance-analysis-of-bayesian-hierarchical-models-versus-classical-statistical-approaches-in-predicting-breast-cancer-treatment-outcomes-evidence-from-kenyan-healthcare-settings\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Comparative Performance Analysis of Bayesian Hierarchical Models Versus Classical Statistical Approaches in Predicting Breast Cancer Treatment Outcomes: Evidence from Kenyan Healthcare Settings - School of Graduate Studies\" \/>\n<meta property=\"og:description\" content=\"2025 Authors Muhati Nelson Lwoyelo Richard Simwa Vincent Marani VIEW ON PUBLISHER SITE Abstract Current breast cancer treatment prediction models inadequately quantify uncertainty and fail to account for institutional clustering effects, particularly in resource-constrained healthcare settings. This study compared the performance of Bayesian hierarchical models against classical frequentist approaches for predicting pathological complete response (pCR) [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/kibu.ac.ke\/sgs\/comparative-performance-analysis-of-bayesian-hierarchical-models-versus-classical-statistical-approaches-in-predicting-breast-cancer-treatment-outcomes-evidence-from-kenyan-healthcare-settings\/\" \/>\n<meta property=\"og:site_name\" content=\"School of Graduate Studies\" \/>\n<meta property=\"article:published_time\" content=\"2025-12-09T11:00:15+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-12-09T11:02:03+00:00\" \/>\n<meta name=\"author\" content=\"kibabii\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"kibabii\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/kibu.ac.ke\/sgs\/comparative-performance-analysis-of-bayesian-hierarchical-models-versus-classical-statistical-approaches-in-predicting-breast-cancer-treatment-outcomes-evidence-from-kenyan-healthcare-settings\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/kibu.ac.ke\/sgs\/comparative-performance-analysis-of-bayesian-hierarchical-models-versus-classical-statistical-approaches-in-predicting-breast-cancer-treatment-outcomes-evidence-from-kenyan-healthcare-settings\/\"},\"author\":{\"name\":\"kibabii\",\"@id\":\"https:\/\/kibu.ac.ke\/sgs\/#\/schema\/person\/3c7b50037f622e72b7023aafee6ed8f8\"},\"headline\":\"Comparative Performance Analysis of Bayesian Hierarchical Models Versus Classical Statistical Approaches in Predicting Breast Cancer Treatment Outcomes: Evidence from Kenyan Healthcare Settings\",\"datePublished\":\"2025-12-09T11:00:15+00:00\",\"dateModified\":\"2025-12-09T11:02:03+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/kibu.ac.ke\/sgs\/comparative-performance-analysis-of-bayesian-hierarchical-models-versus-classical-statistical-approaches-in-predicting-breast-cancer-treatment-outcomes-evidence-from-kenyan-healthcare-settings\/\"},\"wordCount\":228,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/kibu.ac.ke\/sgs\/#organization\"},\"articleSection\":[\"Publications\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/kibu.ac.ke\/sgs\/comparative-performance-analysis-of-bayesian-hierarchical-models-versus-classical-statistical-approaches-in-predicting-breast-cancer-treatment-outcomes-evidence-from-kenyan-healthcare-settings\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/kibu.ac.ke\/sgs\/comparative-performance-analysis-of-bayesian-hierarchical-models-versus-classical-statistical-approaches-in-predicting-breast-cancer-treatment-outcomes-evidence-from-kenyan-healthcare-settings\/\",\"url\":\"https:\/\/kibu.ac.ke\/sgs\/comparative-performance-analysis-of-bayesian-hierarchical-models-versus-classical-statistical-approaches-in-predicting-breast-cancer-treatment-outcomes-evidence-from-kenyan-healthcare-settings\/\",\"name\":\"Comparative Performance Analysis of Bayesian Hierarchical Models Versus Classical Statistical Approaches in Predicting Breast Cancer Treatment Outcomes: Evidence from Kenyan Healthcare Settings - 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