{"id":59,"date":"2009-07-08T11:45:10","date_gmt":"2009-07-08T10:45:10","guid":{"rendered":"https:\/\/blogs.bmj.com\/adc-archimedes\/?p=59"},"modified":"2009-09-06T21:21:14","modified_gmt":"2009-09-06T20:21:14","slug":"where-did-i-leave-that-data","status":"publish","type":"post","link":"https:\/\/blogs.bmj.com\/adc\/2009\/07\/08\/where-did-i-leave-that-data\/","title":{"rendered":"Where did I leave that data?"},"content":{"rendered":"<p class=\"MsoNormal\"><img loading=\"lazy\" decoding=\"async\" class=\"alignright\" style=\"float: right\" src=\"http:\/\/kotaku.com\/assets\/images\/kotaku\/2008\/07\/pieces.jpg\" alt=\"\" width=\"125\" height=\"85\" \/>Data can be lost or go missing for lots of different reasons, and it&#8217;s quite important to know why as it might make you fundamentally muck-up the results of your study of you deal with it badly.<\/p>\n<p class=\"MsoNormal\">The most obvious reason for data to get lost is by bad luck, for example a freak accident like power failure in the lab meaning a blood test can\u2019t be analysed. In this setting, the data are missing for no reason but random chance, and are described as \u201cmissing completely at random\u201d (MCAR). <!--more-->At the other end of the scale, data may be missing for reasons that are extremely understandable and intimately linked to the patient\u2019s condition, for example having only arterial blood gas measurements on the sickest children in a cohort. These missing values, which are related to know other known and measured factors, are confusingly called \u201cmissing not at random\u201d (MNAR). There can also be situations where things are not recorded explain the difference between the missing and the recorded. It may be that patients presenting during the first few weeks of a new resident joining a hospital team are less likely to have all the correct blood tests done as the admitting doctor is not familiar with the study protocol. There will be a systematic difference between those with missing data and those without (new doc vs. old hand) but the reason why the data was missing isn\u2019t linked to anything the researchers can know about (assuming no-one tells them the doctors changed jobs). This type of missing-ness is called \u201cmissing at random\u201d (MAR).<\/p>\n<p class=\"MsoNormal\">While the categorisation of why the data isn\u2019t there is interesting in itself, it also provides a window into opportunities to deal with the problem. MCAR cases can be ignored (using a \u2018complete case\u2019 or \u2018available case\u2019 analysis) and although it will reduce the number of episodes, it doesn\u2019t introduce bias. Undertaking this type of \u2018available case\u2019 analysis when there is a MNAR or MAR problem introduces a form of selection bias.\u00a0 With MAR data, the development of imputation techniques, where the missing elements are replaced by one of a number of reasoned methods, provides a way of avoiding the problems of bias. When it&#8217;s MNAR, you&#8217;re sort of stuffed, and you have to hope it&#8217;s just a teeny tiny bit that&#8217;s not there. (For a readable introduction to these techniques, see <a title=\"PubMed Abstract\" href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/16980149?dopt=Citation\">Donders<\/a>&lt;!&#8211;[if supportFields]&amp;gt;<span style=\"Calibri\"><span style=\"field-begin\"><\/span> ADDIN EN.CITE &amp;lt;EndNote&amp;gt;&amp;lt;Cite ExcludeYear=&quot;1&quot;&amp;gt;&amp;lt;Author&amp;gt;Donders&amp;lt;\/Author&amp;gt;&amp;lt;Year&amp;gt;2006&amp;lt;\/Year&amp;gt;&amp;lt;RecNum&amp;gt;681&amp;lt;\/RecNum&amp;gt;&amp;lt;record&amp;gt;&amp;lt;rec-number&amp;gt;681&amp;lt;\/rec-number&amp;gt;&amp;lt;foreign-keys&amp;gt;&amp;lt;key app=&quot;EN&quot; db-id=&quot;v9f0rzat49wrd9etwatvtsvfw9app055xps0&quot;&amp;gt;681&amp;lt;\/key&amp;gt;&amp;lt;\/foreign-keys&amp;gt;&amp;lt;ref-type name=&quot;Journal Article&quot;&amp;gt;17&amp;lt;\/ref-type&amp;gt;&amp;lt;contributors&amp;gt;&amp;lt;authors&amp;gt;&amp;lt;author&amp;gt;Donders, A. R.&amp;lt;\/author&amp;gt;&amp;lt;author&amp;gt;van der Heijden, G. J.&amp;lt;\/author&amp;gt;&amp;lt;author&amp;gt;Stijnen, T.&amp;lt;\/author&amp;gt;&amp;lt;author&amp;gt;Moons, K. G.&amp;lt;\/author&amp;gt;&amp;lt;\/authors&amp;gt;&amp;lt;\/contributors&amp;gt;&amp;lt;auth-address&amp;gt;Center for Biostatistics, Utrecht University, Utrecht, The Netherlands. R.Donders@geo.uu.nl&amp;lt;\/auth-address&amp;gt;&amp;lt;titles&amp;gt;&amp;lt;title&amp;gt;Review: a gentle introduction to imputation of missing values&amp;lt;\/title&amp;gt;&amp;lt;secondary-title&amp;gt;J Clin Epidemiol&amp;lt;\/secondary-title&amp;gt;&amp;lt;\/titles&amp;gt;&amp;lt;periodical&amp;gt;&amp;lt;full-title&amp;gt;J Clin Epidemiol&amp;lt;\/full-title&amp;gt;&amp;lt;\/periodical&amp;gt;&amp;lt;pages&amp;gt;1087-91&amp;lt;\/pages&amp;gt;&amp;lt;volume&amp;gt;59&amp;lt;\/volume&amp;gt;&amp;lt;number&amp;gt;10&amp;lt;\/number&amp;gt;&amp;lt;edition&amp;gt;2006\/09\/19&amp;lt;\/edition&amp;gt;&amp;lt;keywords&amp;gt;&amp;lt;keyword&amp;gt;Bias (Epidemiology)&amp;lt;\/keyword&amp;gt;&amp;lt;keyword&amp;gt;*Data Interpretation, Statistical&amp;lt;\/keyword&amp;gt;&amp;lt;keyword&amp;gt;Humans&amp;lt;\/keyword&amp;gt;&amp;lt;keyword&amp;gt;Logistic Models&amp;lt;\/keyword&amp;gt;&amp;lt;keyword&amp;gt;Research Design&amp;lt;\/keyword&amp;gt;&amp;lt;\/keywords&amp;gt;&amp;lt;dates&amp;gt;&amp;lt;year&amp;gt;2006&amp;lt;\/year&amp;gt;&amp;lt;pub-dates&amp;gt;&amp;lt;date&amp;gt;Oct&amp;lt;\/date&amp;gt;&amp;lt;\/pub-dates&amp;gt;&amp;lt;\/dates&amp;gt;&amp;lt;isbn&amp;gt;0895-4356 (Print)&amp;lt;\/isbn&amp;gt;&amp;lt;accession-num&amp;gt;16980149&amp;lt;\/accession-num&amp;gt;&amp;lt;urls&amp;gt;&amp;lt;related-urls&amp;gt;&amp;lt;url&amp;gt;http:\/\/www.ncbi.nlm.nih.gov\/entrez\/query.fcgi?cmd=Retrieve&amp;amp;db=PubMed&amp;amp;dopt=Citation&amp;amp;list_uids=16980149&amp;lt;\/url&amp;gt;&amp;lt;\/related-urls&amp;gt;&amp;lt;\/urls&amp;gt;&amp;lt;electronic-resource-num&amp;gt;S0895-4356(06)00197-1 [pii]&amp;#xD;10.1016\/j.jclinepi.2006.01.014&amp;lt;\/electronic-resource-num&amp;gt;&amp;lt;language&amp;gt;eng&amp;lt;\/language&amp;gt;&amp;lt;\/record&amp;gt;&amp;lt;\/Cite&amp;gt;&amp;lt;\/EndNote&amp;gt;<span style=\"field-separator\"><\/span><\/span>&amp;lt;![endif]&#8211;&gt;.)<\/p>\n<p class=\"MsoNormal\">Reference:<\/p>\n<p class=\"MsoNormal\">1. Donders AR, van der Heijden GJ, Stijnen T, Moons KG. Review: a gentle introduction to imputation of missing values. J Clin Epidemiol 2006;59(10):1087-91.<\/p>\n<p class=\"MsoNormal\">Acknowledgement: Photo from <a href=\"http:\/\/kotaku.com\/\">Kotaku.com<\/a> and released under a <a href=\"http:\/\/creativecommons.org\/licenses\/by-nc\/3.0\/\">CreativeCommons Licence<\/a><\/p>\n<p><!--TrendMD v2.4.8--><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Data can be lost or go missing for lots of different reasons, and it&#8217;s quite important to know why as it might make you fundamentally muck-up the results of your study of you deal with it badly. The most obvious reason for data to get lost is by bad luck, for example a freak accident [&#8230;]<\/p>\n<p><a class=\"btn btn-secondary understrap-read-more-link\" href=\"https:\/\/blogs.bmj.com\/adc\/2009\/07\/08\/where-did-i-leave-that-data\/\">Read More&#8230;<\/a><\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[79],"tags":[],"class_list":["post-59","post","type-post","status-publish","format-standard","hentry","category-archimedes"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Where did I leave that data? - ADC Online Blog<\/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:\/\/blogs.bmj.com\/adc\/2009\/07\/08\/where-did-i-leave-that-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Where did I leave that data? - ADC Online Blog\" \/>\n<meta property=\"og:description\" content=\"Data can be lost or go missing for lots of different reasons, and it&#8217;s quite important to know why as it might make you fundamentally muck-up the results of your study of you deal with it badly. 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