Abstract
We demonstrate how to apply regression risk analysis to compute risk ratios and risk differences from logistic regression models using complex survey data. We validate the use of regression risk analysis for complex survey design. First, we derive formulas for adjusted risk ratios (ARRs) and adjusted risk differences (ARDs) adjusted for weighting, stratification, and clustering. Then we use Monte Carlo data to show why correcting statistics for complex survey design is important. We show how to calculate and interpret ARRs using a publicly available data set with a binary outcome. Regression risk analysis can be applied to complex survey data to calculate correct ARRs and ARDs from logistic regression.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Altman, D.G., Deeks, J.J., Sackett, D.L.: Odds ratios should be avoided when events are common. Br. Med. J. 317, 1318 (1998)
Botman, S.L., Moore, T.F., Moriarity, C.L., Parsons, V.L.: Design and estimation for the National Health Interview Survey, 1995–2004. National Center for Health Statistics. Vital Health Stat. 2(130), 1–31 (2000)
Greenland, S.: Interpretation and choice of effect measures in epidemiologic analyses. Am. J. Epidemiol. 125(5), 761–768 (1987)
Kleinman, L.C., Norton, E.C.: What’s the Risk? A simple approach for estimating adjusted risk ratios from nonlinear models including logistic regression. Health Serv. Res. 44(1), 288–302 (2009)
Korn, E.L., Graubard, B.I.: Analysis of Health Surveys. Wiley, New York (1999)
Levy, P.S., Lemeshow, S.: Sampling of Populations: Methods and Applications, 2nd edn. Wiley, Hoboken (1991)
Norton, E.C., Miller, M.M., Kleinman, L.C.: Computing adjusted risk ratios and risk differences in Stata. Stata J. 13(3), 492–509 (2013)
Rao, P.: Sampling Methodologies with Applications. Chapman and Hall, New York (2000)
Sackett, D.L., Deeks, J.J., Altman, D.G.: Down with odds ratios! Evid. Based Med. 1, 164–166 (1996)
Särndal, C.-E., Swensson, B., Wretman, J.: Model Assisted Survey Sampling. Springer, New York (1992)
Schwartz, L.M., Woloshin, S., Welch, H.G.: Misunderstandings about the effects of race and sex on physicians’ referrals for cardiac catheterization. N. Engl. J. Med. 341(4), 279–283 (1999)
Tajeu, G.S., Sen, B., Allison, D.B., Menachemi, N.: Misuse of odds ratios in obesity literature: an empirical analysis of published studies. Obesity 20(8), 1726–1731 (2012)
Walter, S.D.: Choice of effect measure for epidemiological data. J. Clin. Epidemiol. 53, 931–939 (2000)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Norton, E.C., Carroll, N.W., Miller, M.M. et al. Computing risk ratios from data with complex survey design. Health Serv Outcomes Res Method 14, 3–14 (2014). https://doi.org/10.1007/s10742-014-0114-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10742-014-0114-0