![]() We also apply our proposed methods in a comparison of glucagon-like peptide-1 receptor agonists therapy and insulin therapy among patients with type 2 diabetes, using the UK Clinical Practice Research Datalink data.Ĭausal inference confounding doubly robust estimator propensity score weighting. We demonstrate the properties of the proposed estimators through theoretical proof and simulation studies. The second proposed estimator is doubly robust if the target function has a linear dependence on the propensity score, which can be used to estimate the average treatment effect for the treated and the average treatment effect for the control. The first proposed estimator is doubly robust if the target function is known or can be correctly specified. In this paper, we propose two estimators based on augmented inverse probability weighting to estimate the weighted average treatment effect for a well-defined target population (ie, there exists a predefined target function of covariates that characterizes the population of interest, for example, a function of age to focus on elderly diabetic patients using samples from the US population). For instance, when the goal is to introduce a new treatment to a target population, the question is what efficacy (or effectiveness) can be gained by switching patients from a standard of care (control) to this new treatment, for which the average treatment effect for the control estimand can be applied. #Average treatment effect on the treated free#PMID: 35637426 Free PMC article.The weighted average treatment effect is a causal measure for the comparison of interventions in a specific target population, which may be different from the population where data are sampled from. Lefort M, Sharmin S, Andersen JB, Vukusic S, Casey R, Debouverie M, Edan G, Ciron J, Ruet A, De Sèze J, Maillart E, Zephir H, Labauge P, Defer G, Lebrun-Frenay C, Moreau T, Berger E, Clavelou P, Pelletier J, Stankoff B, Gout O, Thouvenot E, Heinzlef O, Al-Khedr A, Bourre B, Casez O, Cabre P, Montcuquet A, Wahab A, Camdessanché JP, Maurousset A, Ben Nasr H, Hankiewicz K, Pottier C, Maubeuge N, Dimitri-Boulos D, Nifle C, Laplaud DA, Horakova D, Havrdova EK, Alroughani R, Izquierdo G, Eichau S, Ozakbas S, Patti F, Onofrj M, Lugaresi A, Terzi M, Grammond P, Grand'Maison F, Yamout B, Prat A, Girard M, Duquette P, Boz C, Trojano M, McCombe P, Slee M, Lechner-Scott J, Turkoglu R, Sola P, Ferraro D, Granella F, Shaygannejad V, Prevost J, Maimone D, Skibina O, Buzzard K, Van der Walt A, Karabudak R, Van Wijmeersch B, Csepany T, Spitaleri D, Vucic S, Koch-Henriksen N, Sellebjerg F, Soerensen PS, Hilt Christensen CC, Rasmussen PV, Jensen MB, Frederiksen JL, Bramow S, Mathiesen HK, Schreiber KI, Butzkueven H, Magyari M, Kalincik T, Leray E. The choice of the estimand should drive the choice of the method.Īverage treatment effect average treatment on the treated inverse probability weighting matching positivity assumption propensity score. In Section VI, I discuss a number of implementations of average treatment effect estimators. overlap between the treated and control observations. We show that this empirical result is supported by theory.Īlthough both approaches are recommended as valid methods for causal inference, propensity score-matching for ATT and inverse probability of treatment weighting for average treatment effect yield substantially different estimates of treatment effect. average treatment effects under weaker assumptions than typical of the earlier literature by avoiding distributional and functional-form assump. However, when treatment effect varies according to the propensity score (E is not constant, Y being the outcome and g(X) the propensity score), propensity score matching ATT estimate could strongly differ from the inverse probability of treatment weighting-average treatment effect estimate. From simulations, we concluded that positivity bias was of limited magnitude and did not explain the large differences in the point estimates. Potential reasons were (1) violation of the positivity assumption (2) treatment effect was not uniform across the distribution of the propensity score. ![]() We used Monte Carlo simulations to investigate the important differences in the two estimates.Ĭontinuous positive airway pressure application increased hospital mortality overall, but no continuous positive airway pressure effect was found on the treated. We applied the two propensity score methods to assess the effect of continuous positive airway pressure on mortality in patients hospitalized for acute heart failure. We illustrate how different estimands can result in very different conclusions. Propensity score matching is typically used to estimate the average treatment effect for the treated while inverse probability of treatment weighting aims at estimating the population average treatment effect. ![]()
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