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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: BACKGROUND, METHODS, RESULTS. Meshes: Confounding Factors (Epidemiology), Data Interpretation, Statistical, Humans, Likelihood Functions, Logistic Models, Models, Statistical, Randomized Controlled Trials as Topic, Treatment Outcome.
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Does systematic differences in treatment effect estimate between propensity score methods and logistic regression? Context: 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. 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. 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.
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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. Final Decision: yes