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

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 sh...

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Bibliographic Details
Published in:International journal of epidemiology 2008-10, Vol.37 (5), p.1142-1147
Main Authors: Martens, Edwin P, Pestman, Wiebe R, de Boer, Anthonius, Belitser, Svetlana V, Klungel, Olaf H
Format: Article
Language:English
Subjects:
adjusted treatment effect
Biological and medical sciences
conditional treatment effect
confounding
Confounding Factors (Epidemiology)
Data Interpretation, Statistical
Humans
Likelihood Functions
Logistic Models
logistic regression
marginal treatment effect
Medical sciences
Miscellaneous
Models, Statistical
observational studies
Propensity scores
Public health. Hygiene
Public health. Hygiene-occupational medicine
Randomized Controlled Trials as Topic
Treatment Outcome
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Summary: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.
ISSN:0300-5771
1464-3685
DOI:10.1093/ije/dyn079