{"id":593,"date":"2019-03-22T09:52:18","date_gmt":"2019-03-22T09:52:18","guid":{"rendered":"https:\/\/blogs.kcl.ac.uk\/cancerprevention\/?page_id=593"},"modified":"2019-03-22T09:52:18","modified_gmt":"2019-03-22T09:52:18","slug":"using-roc-curves-to-identify-the-most-accurate-test","status":"publish","type":"page","link":"https:\/\/cancerprevention.qmul.ac.uk\/index.php\/using-roc-curves-to-identify-the-most-accurate-test\/","title":{"rendered":"Using ROC curves to identify the most accurate test"},"content":{"rendered":"

A\u00a0receiver operating characteristic curve<\/b>, or\u00a0ROC curve<\/b>, is a\u00a0graphical plot<\/a>\u00a0that illustrates the diagnostic ability of a\u00a0binary classifier<\/a>\u00a0system as its discrimination threshold is varied.<\/p>\n

The relationship between sensitivity and specificity can be represented in a ROC graph. This is created by plotting the sensitivity (true positive rate) on the vertical axis against the false positive rate (1-specifcity) on the horizontal axis, for every observed data value. The area under this curve (AUC) represent the overall accuracy of the test. The AUC value lies between 0 and 1 and the closer the value is to 1 the better the test. An AUC with a value of 0.5 suggests no discrimination.<\/p>\n

The relationship between sensitivity and specificity can be represented in a ROC graph. This is created by plotting the sensitivity (true positive rate) on the vertical axis against the false positive rate (1-specifcity) on the horizontal axis, for every observed data value. The area under this curve (AUC) represent the overall accuracy of the test. The AUC value lies between 0 and 1 and the closer the value is to 1 the better the test. An AUC with a value of 0.5 suggests no discrimination.<\/p>\n

By way of illustration a hypothetical scenario is presented below.<\/p>\n

Measurement of serum levels of Prostate Specific Antigen (PSA) is used as a test for detecting prostate cancer. Improvements in technology have led to two new biomarkers that could potentially detect prostate cancer.<\/p>\n

A hypothetical investigation was carried out to discover whether biomarker A (Apsa) and B (Bpsa) can be used to detect prostate cancer and how they compare to the standard test (Spsa). Blood samples from 100,000 men were tested at time 0 with all three tests. These men were then followed up for 15 years and deaths from prostate cancer were recorded.<\/p>\n

A ROC curve was used to compare biomarker A and B to the standard test (Spsa). The ROC curve shows the trade-off between sensitivity and specificity. It shows that the standard test is still the most accurate test (AUC=0.9125) in identifying men with prostate cancer, and biomarker B (AUC=0.5123) is the worst and shouldn\u2019t be considered as a test for identifying men with prostate cancer.<\/p>\n

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ROC analysis of three hypothetical tests to identify prostate cancer<\/figcaption><\/figure>\n","protected":false},"excerpt":{"rendered":"

A\u00a0receiver operating characteristic curve, or\u00a0ROC curve, is a\u00a0graphical plot\u00a0that illustrates the diagnostic ability of a\u00a0binary classifier\u00a0system as its discrimination threshold is varied. The relationship between sensitivity and specificity can be represented in a ROC graph. This is created by plotting the sensitivity (true positive rate) on the vertical axis against the false positive rate (1-specifcity)… Read More »Using ROC curves to identify the most accurate test<\/span><\/a><\/p>\n","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","cybocfi_hide_featured_image":"","footnotes":""},"class_list":["post-593","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/cancerprevention.qmul.ac.uk\/index.php\/wp-json\/wp\/v2\/pages\/593","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/cancerprevention.qmul.ac.uk\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/cancerprevention.qmul.ac.uk\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/cancerprevention.qmul.ac.uk\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/cancerprevention.qmul.ac.uk\/index.php\/wp-json\/wp\/v2\/comments?post=593"}],"version-history":[{"count":0,"href":"https:\/\/cancerprevention.qmul.ac.uk\/index.php\/wp-json\/wp\/v2\/pages\/593\/revisions"}],"wp:attachment":[{"href":"https:\/\/cancerprevention.qmul.ac.uk\/index.php\/wp-json\/wp\/v2\/media?parent=593"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}