Proportions, odds, and risk.

Perhaps the most common and familiar way that the results of medical research and epidemiologic investigations are summarized is in a table of counts. Numbers of subjects with and without the outcome of interest are listed for each treatment or risk factor group. By using the study sample data thus tabulated, investigators quantify the association between treatment or risk factor and outcome. Three simple statistical calculations are used for this purpose: difference in proportions, relative risk, and odds ratio. The appropriate use of these statistics to estimate the association between treatment or risk factor and outcome in the relevant population depends on the design of the research. Herein, the enumeration of proportions, odds ratios, and risks and the relationships between them are demonstrated, along with guidelines for use and interpretation of these statistics appropriate to the type of study that gives rise to the data.

[1]  Lisa M. Schwartz,et al.  Misunderstandings about the effects of race and sex on physicians' referrals for cardiac catheterization. , 1999, The New England journal of medicine.

[2]  C. Langlotz.  Fundamental measures of diagnostic examination performance: usefulness for clinical decision making and research. , 2003, Radiology.

[3]  E. H. Simpson,et al.  The Interpretation of Interaction in Contingency Tables , 1951 .

[4]  J. T. ten Cate,et al.  ACQUIRED RISK FACTORS AND DEEP VEIN THROMBOSIS IN SYMPTOMATIC OUT PATIENTS , 1994, Thrombosis and Haemostasis.

[5]  W. G. Cochran.  Some Methods for Strengthening the Common χ 2 Tests , 1954 .

[6]  J. Ludvigsson.  Book Review : Studying a Study and Testing a Test by RK Riegelman, Lippincott Williams & Wilkins 2000. , 2001 .

[7]  J. Ying,et al.  Hypothesis testing III: counts and medians. , 2003, Radiology.

[8]  W. Haenszel,et al.  Statistical aspects of the analysis of data from retrospective studies of disease. , 1959, Journal of the National Cancer Institute.

[9]  J. Elmore,et al.  Effect of False-Positive Mammograms on Interval Breast Cancer Screening in a Health Maintenance Organization , 1999, Annals of Internal Medicine.

[10]  A. Agresti,et al.  Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.

[11]  L. McMahon,et al.  Personalized Targeted Mailing Increases Mammography Among Long-Term Noncompliant Medicare Beneficiaries: A Randomized Trial , 2003, Medical care.

[12]  K A Schulman,et al.  The effect of race and sex on physicians' recommendations for cardiac catheterization. , 1999, The New England journal of medicine.

[13]  Celine M Vachon,et al.  Case-control study of increased mammographic breast density response to hormone replacement therapy. , 2002, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[14]  D. Zurakowski,et al.  Measurement variability and confidence intervals in medicine: why should radiologists care? , 2003, Radiology.

[15]  D. Sackett,et al.  The Ends of Human Life: Medical Ethics in a Liberal Polity , 1992, Annals of Internal Medicine.