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American Journal of Epidemiology Advance Access originally published online on January 19, 2009
American Journal of Epidemiology 2009 169(7):909-917; doi:10.1093/aje/kwn391
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American Journal of Epidemiology © 2009 The Authors
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

PRACTICE OF EPIDEMIOLOGY

Different Methods of Balancing Covariates Leading to Different Effect Estimates in the Presence of Effect Modification

Mark Lunt, Daniel Solomon, Kenneth Rothman, Robert Glynn, Kimme Hyrich, Deborah P. M. Symmons, Til Stürmer and the British Society for Rheumatology Biologics Register, the British Society for Rheumatology Biologics Register Control Centre Consortium

Correspondence to Dr. Mark Lunt, arc Epidemiology Unit, University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PT, United Kingdom (e-mail: mark.lunt{at}manchester.ac.uk).

Received for publication May 9, 2008. Accepted for publication November 20, 2008.

A number of covariate-balancing methods, based on the propensity score, are widely used to estimate treatment effects in observational studies. If the treatment effect varies with the propensity score, however, different methods can give very different answers. The authors illustrate this effect by using data from a United Kingdom–based registry of subjects treated with anti–tumor necrosis factor drugs for rheumatoid arthritis. Estimates of the effect of these drugs on mortality varied from a relative risk of 0.4 (95% confidence interval: 0.16, 0.91) to a relative risk of 1.3 (95% confidence interval: 0.8, 2.25), depending on the balancing method chosen. The authors show that these differences were due to a combination of an interaction between propensity score and treatment effect and to differences in weighting subjects with different propensity scores. Thus, the methods are being used to calculate average treatment effects in populations with very different distributions of effect-modifying variables, resulting in different overall estimates. This phenomenon highlights the importance of careful selection of the covariate-balancing method so that the overall estimate has a meaningful interpretation.

covariate balance; effect modification; observational study; propensity score; weighting

Abbreviations: TNF, tumor necrosis factor


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