Using Bayes' nomogram to help interpret odds ratios
John Page, MBBS, MSc, John Attia, MD, PhD, FRCPC · ACP Journal Club · 2003
EditorialSeptember 1, 2003Using Bayes' nomogram to help interpret odds ratiosJohn Page, MBBS, MSc, John Attia, MD, PhD, FRCPCJohn Page, MBBS, MScHarvard University School of Public Health, Boston, Massachusetts, USA (J.P.), John Attia, MD, PhD, FRCPCUniversity of Newcastle, Newcastle, New South Wales, Australia (J.A.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/ACPJC-2003-139-2-A11 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack Citations ShareFacebookTwitterLinkedInRedditEmail In certain scenarios, the odds ratio (OR) provides an unbiased estimate of the rate ratio in case–control studies (1). However, the OR is also frequently used to estimate the risk ratio (relative risk) (RR) of an outcome in the presence of a risk factor. The degree of error in this estimate is frequently small but can sometimes be substantial. The OR as an estimate of the RR always overestimates the effect of the exposure (i.e., results in an estimate farther from 1). The degree of divergence between the OR and the RR depends on the size of the OR and ...References1 Rothman KJ, Greenland S. Modern Epidemiology. 2d ed. Philadelphia: Lippincott-Raven; 1998. Google Scholar2 Davies HT, Crombie IK, Tavakoli M. When can odds ratios mislead? BMJ.1998;316:989-91. 9550961 Google Scholar3 Zhang J, Yu KF. What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. JAMA. 1998;280:1690-1. 9832001 Google Scholar4 Sinclair JC, Bracken MB. Clinically useful measures of treatment effect in binary analyses of randomized trials. J Clin Epidemiol. 1994;47:881-9. 7730891 Google Scholar5 Fagan TJ. Letter: Nomogram for Bayes theorem. N Engl J Med. 1975;293:257. Google Scholar6 Fletcher RH, Fletcher SW, Wagner EH. Clinical Epidemiology: The Essentials. Baltimore: Williams & Wilkins; 1996. Google Scholar7 Sackett DL, Haynes RB, Guyatt GH, Tugwell P. Clinical Epidemiology: A Basic Science for Clinical Medicine. 2d ed. Boston: Little, Brown; 1991. Google Scholar8 Laupacis A, Sackett DL, Roberts RS. An assessment of clinically useful measures of the consequences of treatment. N Engl J Med. 1988;318:1728-33. 3374545 Google Scholar9 Bjerre LM, LeLorier J. Expressing the magnitude of adverse effects in case-control studies: "the number of patients needed to be treated for one additional patient to be harmed." BMJ. 2000;320:503-6. 10678870 Google Scholar10 Page J, Henry D. Consumption of NSAIDs and the development of congestive heart failure in elderly patients: an underrecognized public health problem. Arch Intern Med. 2000;160:777-84. 10737277 Google Scholar11 Kannel WB, D'Agostino RB, Silbershatz H, et al. Profile for estimating risk of heart failure. Arch Intern Med. 1999;159:1197-204. 10371227 Google Scholar12 Laine L, Cook D. Endoscopic ligation compared with sclerotherapy for treatment of esophageal variceal bleeding. A meta-analysis. Ann Intern Med. 1995;123:280-7. 7611595 Google Scholar Author, Article, and Disclosure InformationAffiliations: Harvard University School of Public Health, Boston, Massachusetts, USA (J.P.)University of Newcastle, Newcastle, New South Wales, Australia (J.A.) Previousarticle Advertisement FiguresReferencesRelatedDetails September 1, 2003Volume 139, Issue 2Page: A-11KeywordsArthritisBlood pressureCardiovascular therapyCoronary heart diseaseDatabasesDiagnostic radiologyDrugsElectrocardiographyEpidemiologyEvidence based medicineHeart failureHeart rateHemorrhageLigation assayMedical risk factorsNSAIDsSystolic pressureThorax ePublished: 9 March 2020 Issue Published: September 1, 2003 Copyright & PermissionsCopyright © 2003 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...