{"id":1669,"date":"2024-01-09T11:59:48","date_gmt":"2024-01-09T16:59:48","guid":{"rendered":"https:\/\/blog.uvm.edu\/tbplante\/?p=1669"},"modified":"2024-01-09T12:18:27","modified_gmt":"2024-01-09T17:18:27","slug":"printing-hazard-ratio-on-kaplan-meier-curve-in-stata","status":"publish","type":"post","link":"https:\/\/blog.uvm.edu\/tbplante\/2024\/01\/09\/printing-hazard-ratio-on-kaplan-meier-curve-in-stata\/","title":{"rendered":"Printing hazard ratio on Kaplan Meier curve in Stata"},"content":{"rendered":"\n

I recently made a figure that estimates a hazard ratio and renders it right on top of a Kaplan Meier curve in Stata. Here’s some example code to make this.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Good luck!<\/p>\n\n\n\n

\n\/\/ Load example dataset. I got this from the --help stset-- file\nwebuse diet, clear\n\n\/\/ First, stset the data. \nstset dox \/\/\/ dox is the event or censor date\n, \/\/\/\nfailure(fail) \/\/\/ \"fail\" is the failure vs censor variable\nscale(365.25)\n\n\n\/\/ Next, estimate a cox ph model by \"hienergy\"\nstcox hienergy\n\/\/ now grab the bits from output of this\nlocal hrb=r(table)[1,1]\nlocal hrlo=r(table)[5,1]\nlocal hrhi=r(table)[6,1]\nlocal pval = r(table)[4,1]\n\/\/ now format the p-value so it's pretty\nif `pval'>=0.056 {\n\tlocal pvalue \"P=`: display %3.2f `pval''\"\n}\nif `pval'>=0.044 & `pval'<0.056 {\n\tlocal pvalue "P=`: display %5.4f `pval''"\n}\nif `pval' <0.044 {\n\tlocal pvalue "P=`: display %4.3f `pval''"\n}\nif `pval' <0.001 {\n\tlocal pvalue "P<0.001"\n}\nif `pval' <0.0001 {\n\tlocal pvalue "P<0.0001"\n}\n\ndi "original P is " `pval' ", formatted is " "`pvalue'"\ndi "HR " %4.2f `hrb' " (95% CI " %4.2f `hrlo' "-" %4.2f `hrhi' "; `pvalue')"\n\n\/\/ Now make a km plot. this example uses CIs\nsts graph \/\/\/\n, \/\/\/\nsurvival \/\/\/ \nby(hienergy) \/\/\/\nplot1opts(lpattern(dash) lcolor(red)) \/\/\/ options for line 1\nplot2opts(lpattern(solid) lcolor(blue)) \/\/\/ options for line 2\nci \/\/\/ add CIs\nci1opts(color(red%20)) \/\/\/ options for CI 1\nci2opts(color(blue%20)) \/\/\/ options for CI 2\n\/\/\/ Following this is the legend, placed in the 6 O'clock position. \n\/\/\/ Only graphics 5 and 6 are needed, but all 6 are shown so you \n\/\/\/ see that other bits that can show up in the legend. Delete \n\/\/\/ everything except for 5 and 6 to hide the rest of the legend components\nlegend(order(1 "[one]" 2 "[two]" 3 "[three]" 4 "[four]" 5 "First group" 6 "Second group") position(6)) \/\/\/\n\/\/\/ Risk table to print at the bottom:\nrisktable(0(5)20 , size(small) order(1 "First group" 2 "Second group")) \/\/\/\ntitle("Title") \/\/\/\nt1title("Subtitle") \/\/\/\nxtitle("Year of Follow-Up") \/\/\/\nytitle("Event-Free Survival") \/\/\/\n\/\/\/ Here's how you render the HR. Change the first 2 numbers to move it:\ntext(0 0 "`: display "HR " %4.2f `hrb' " (95% CI " %4.2f `hrlo' "-" %4.2f `hrhi' "; `pvalue')"'", placement(e) size(medsmall)) \/\/\/\nyla(0(0.2)1) <\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"
I recently made a figure that estimates a hazard ratio and renders it right on top of a Kaplan Meier curve in Stata. Here’s some example code to make this. Good luck!<\/p>\n","protected":false},"author":4473,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[477491],"tags":[703490,703489,502556,703492,703491],"class_list":["post-1669","post","type-post","status-publish","format-standard","hentry","category-stata-code","tag-cox-proportional-hazards-models","tag-hazard-ratio","tag-stata","tag-stcox","tag-stset"],"_links":{"self":[{"href":"https:\/\/blog.uvm.edu\/tbplante\/wp-json\/wp\/v2\/posts\/1669","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.uvm.edu\/tbplante\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.uvm.edu\/tbplante\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.uvm.edu\/tbplante\/wp-json\/wp\/v2\/users\/4473"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.uvm.edu\/tbplante\/wp-json\/wp\/v2\/comments?post=1669"}],"version-history":[{"count":8,"href":"https:\/\/blog.uvm.edu\/tbplante\/wp-json\/wp\/v2\/posts\/1669\/revisions"}],"predecessor-version":[{"id":1696,"href":"https:\/\/blog.uvm.edu\/tbplante\/wp-json\/wp\/v2\/posts\/1669\/revisions\/1696"}],"wp:attachment":[{"href":"https:\/\/blog.uvm.edu\/tbplante\/wp-json\/wp\/v2\/media?parent=1669"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.uvm.edu\/tbplante\/wp-json\/wp\/v2\/categories?post=1669"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.uvm.edu\/tbplante\/wp-json\/wp\/v2\/tags?post=1669"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}