2004
DOI: 10.1002/sim.1903
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Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study

Abstract: Estimation of treatment effects with causal interpretation from observational data is complicated because exposure to treatment may be confounded with subject characteristics. The propensity score, the probability of treatment exposure conditional on covariates, is the basis for two approaches to adjusting for confounding: methods based on stratification of observations by quantiles of estimated propensity scores and methods based on weighting observations by the inverse of estimated propensity scores. We revi… Show more

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Cited by 1,233 publications
(755 citation statements)
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“…Inverse probability of treatment weighting (IPTW) analysis was then used to adjust for differences between the 2 groups based on the propensity scores 25, 26. We used the methods described by Austin and Stuart27 to calculate standardized differences post‐IPTW adjustment to ensure all baseline covariates were equally distributed in the adjusted cohorts.…”
Section: Methodsmentioning
confidence: 99%
“…Inverse probability of treatment weighting (IPTW) analysis was then used to adjust for differences between the 2 groups based on the propensity scores 25, 26. We used the methods described by Austin and Stuart27 to calculate standardized differences post‐IPTW adjustment to ensure all baseline covariates were equally distributed in the adjusted cohorts.…”
Section: Methodsmentioning
confidence: 99%
“…[57][58][59][60][61] These studies have shown matching and IPTW to be more effective than stratification or covariate adjustment. [58][59][60][61] The principal advantage of propensity scoring over other adjustment methods such as regression analysis is that it can be used even with small sample sizes and therefore may be particularly relevant to regenerative medicine. Propensity scoring also has a number of disadvantages.…”
Section: Propensity Scoringmentioning
confidence: 99%
“…Because patient characteristics differed across the categories of consistency in hemoglobin A1c testing (Table 1), we used multilevel inverse propensity weighting to develop a matched cohort of patients, based on the patient's likelihood to receive low‐, medium‐, and high‐consistency testing 26, 27. We developed multinomial logistic models that identified patient and structural factors associated with each category of consistency in hemoglobin A1c testing.…”
Section: Methodsmentioning
confidence: 99%