{"id":49704,"date":"2024-08-26T08:48:16","date_gmt":"2024-08-26T06:48:16","guid":{"rendered":"https:\/\/clariscience.com\/blog\/uncategorized\/come-tratto-i-missing-data"},"modified":"2024-11-08T11:08:01","modified_gmt":"2024-11-08T10:08:01","slug":"come-tratto-i-missing-data","status":"publish","type":"post","link":"https:\/\/clariscience.com\/en\/blog\/scientific-communication\/come-tratto-i-missing-data","title":{"rendered":"How to handle missing data"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"49704\" class=\"elementor elementor-49704 elementor-46786\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-00fe777 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"00fe777\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\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-16e3715\" data-id=\"16e3715\" data-element_type=\"column\" data-e-type=\"column\">\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-ed54a33 elementor-widget elementor-widget-text-editor\" data-id=\"ed54a33\" data-element_type=\"widget\" data-e-type=\"widget\" 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>In any clinical study, researchers often encounter datasets with <strong>missing observations<\/strong>, commonly referred to as <strong>&#8220;missing data.&#8221;<\/strong> Most standard statistical methods require that information is available for all observations for each study variable. Therefore, <strong>managing missing data is crucial, as neglecting them can lead to distorted and unreliable results.<\/strong><\/p><h2>Understanding Missing Data<\/h2><p>The first step in managing missing data is to <strong>understand how frequently they occur.<\/strong> Intuitively, handling a dataset where missing data represent a small percentage is quite different from dealing with a dataset with a significant amount of missing data.<\/p><p>\u00a0<\/p><p>Next, it\u2019s essential to <strong>comprehend the reasons behind the missing data.<\/strong> This aspect is key in interpreting results, as it<strong> allows researchers to distinguish whether the missing data arise from causal dynamics or are associated with specific experimental factors.<\/strong> Based on this criterion, missing data can be classified into three main categories:<\/p><ol><li><strong>Missing Completely At Random (MCAR):<\/strong> In this case, missing data are randomly distributed across the sample and are not related to any study variables.<\/li><li><strong>Missing At Random (MAR):<\/strong> Here, the probability of a missing data point is related to certain variables, but not the value of the missing data itself.<\/li><li><strong>Missing Not At Random (MNAR):<\/strong> This category includes all missing data that depend on both the value of the data itself and certain study variables.<\/li><\/ol><h2>Managing Missing Data<\/h2><p>Ideally, <strong>the best way to manage missing data is to prevent them<\/strong> from occurring in the first place. This requires <strong>careful study design<\/strong> and <strong>accurate data collection<\/strong>. For example, reducing the number of follow-up visits and collecting only essential information at each visit, along with designing easy-to-complete forms, can help minimize missing data. Prior to starting clinical research, it\u2019s advisable to develop a <strong>detailed protocol documentation,<\/strong> including methods for <strong>participant screening, training for researchers and participants, communication among involved parties, and monitoring of collected data.<\/strong> Additionally, it\u2019s possible to establish a priori an acceptable level of missing data.<\/p><p><strong>There are various techniques to handle missing data, fundamentally falling into two approaches: either deleting observations or imputing missing values.<\/strong> Here are some techniques available to researchers:<\/p><p>\u00a0<\/p><ul><li><strong>Listwise Deletion:<\/strong> This method removes cases with missing data and analyzes only the remaining complete data. If the assumption of MCAR is met, this method can produce unbiased estimates.<\/li><li><strong>\u00a0Pairwise Deletion:<\/strong> This method uses available data for each specific analysis, preserving more information than listwise deletion. However, it can produce estimates from different data sets and may lead to analytical issues.<\/li><li><strong>Mean Substitution:<\/strong> Missing values are replaced with the mean of the variable. However, this can introduce bias into the estimates and increase standard error.<\/li><li><strong>Regression Imputation:<\/strong> This method estimates missing values using other variables through regression analysis. It allows for more data retention compared to deletion methods.<\/li><li><strong>Last Observation Carried Forward (LOCF):<\/strong> Each missing value is replaced with the last known observation for that subject. While simple, this method can produce biased estimates of treatment effects.<\/li><li><strong>Maximum Likelihood:<\/strong> This method estimates missing data using observed data from other variables. It can be time-consuming and may yield biased estimates if assumptions are not met.<\/li><\/ul><ul><li><strong>Multiple Imputation:<\/strong> This technique replaces missing data with several plausible values, generating multiple complete datasets. The results of analyses on these datasets are then combined to obtain a final estimate. It is a robust method that produces valid estimates even with a small sample or a high number of missing values.<\/li><\/ul><p>\u00a0<\/p><p>The choice of method should be evaluated by the researcher in relation to the experimental needs and characteristics of the missing data.<\/p><h2>Conclusion<\/h2><p>Missing data present a <strong>significant challenge in clinical research<\/strong>, as they can <strong>compromise the reliability and validity of analyses.<\/strong> Understanding the<strong> nature and frequency of missing data is essential to adopt the best management strategies.<\/strong> Preventing missing data through careful study design and attentive data collection is a crucial first step. If missing data are present, researchers have<strong> several techniques<\/strong> at their disposal to manage them, adapting their approach based on the nature of the missing data.<\/p><h3>Further Reading:<\/h3><p>&#8211; Kang H. The prevention and handling of the missing data. *Korean J Anesthesiol*. 2013 May;64(5):402-6. doi: 10.4097\/kjae.2013.64.5.402.<\/p><p>&#8211; Heymans MW, Twisk JWR. Handling missing data in clinical research. *J Clin Epidemiol*. 2022;151:185-188. doi:10.1016\/j.jclinepi.2022.08.016.<\/p><p>\u00a0<\/p><p>\u00a0<\/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>In any clinical study, researchers often encounter datasets with missing observations, commonly referred to as &#8220;missing data.&#8221; <\/p>\n","protected":false},"author":114,"featured_media":46791,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"episode_type":"","audio_file":"","podmotor_file_id":"","podmotor_episode_id":"","cover_image":"","cover_image_id":"","duration":"","filesize":"","filesize_raw":"","date_recorded":"","explicit":"","block":"","powered_cache_disable_cache":false,"powered_cache_disable_css_optimization":false,"powered_cache_disable_js_optimization":false,"footnotes":""},"categories":[534],"tags":[575],"class_list":["post-49704","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-scientific-communication","tag-magazine-en"],"acf":[],"yoast_head":"<title>How to handle missing data | Clariscience Magazine<\/title>\n<meta name=\"description\" content=\"In any clinical study, researchers often encounter datasets with missing observations, commonly referred to as &quot;missing 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