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IJE Advance Access published online on May 3, 2008
International Journal of Epidemiology, doi:10.1093/ije/dyn079
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Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2008; all rights reserved.

Systematic differences in treatment effect estimates between propensity score methods and logistic regression

Edwin P Martens1,2, Wiebe R Pestman2, Anthonius de Boer1, Svetlana V Belitser1 and Olaf H Klungel1,*

1 Department of Pharmacoepidemiology and Pharmacotherapy, Utrecht Institute of Pharmaceutical Sciences (UIPS), Utrecht University, Utrecht, The Netherlands.
2 Centre for Biostatistics, Utrecht University, Utrecht, The Netherlands.

* Corresponding author. Department of Pharmacoepidemiology and Pharmacotherapy, Utrecht Institute of Pharmaceutical Sciences (UIPS), Utrecht University, Sorbonnelaan 16, 3584 CA Utrecht, The Netherlands. E-mail: o.h.klungel{at}uu.nl


  Abstract

Background In medical research both propensity score methods and logistic regression analysis are used to estimate treatment effects in observational studies. From literature reviews it has been concluded that treatment effect estimates from both methods are quite similar. With this study we will show that there are systematic differences which can be substantial.

Methods We used a simulated population with a known marginal treatment effect and applied a propensity score method and logistic regression analysis to adjust for confounding.

Results The adjusted treatment effect in logistic regression is in general further away from the true marginal treatment effect than the adjusted effect in propensity score methods. The difference is systematic and dependent on the incidence proportion, the number of prognostic factors and the magnitude of the treatment effect. For instance, a substantial difference of 20% is found when the treatment effect is 2.0, the incidence proportion is 0.20 and there are more than 11 prognostic factors.

Conclusions Propensity score methods give in general treatment effect estimates that are closer to the true marginal treatment effect than a logistic regression model in which all confounders are modelled.

Keywords Propensity scores, confounding, adjusted treatment effect, logistic regression, conditional treatment effect, marginal treatment effect, observational studies

Accepted 1 April 2008


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