2013
DOI: 10.1161/circoutcomes.113.000359
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Propensity Score Methods for Confounding Control in Nonexperimental Research

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Cited by 453 publications
(400 citation statements)
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“…First, propensity scores from a logistic regression model with no hierarchical structure were used to estimate treatment effects using inverse probability weighting, which estimates treatment effects in a population with risk factor distribution similar to the full study population (20,21). Second, we developed a hierarchical generalized linear model adjusting for patient characteristics and including a random hospital effect to assess the effect of NIV on the study outcomes.…”
Section: Discussionmentioning
confidence: 99%
“…First, propensity scores from a logistic regression model with no hierarchical structure were used to estimate treatment effects using inverse probability weighting, which estimates treatment effects in a population with risk factor distribution similar to the full study population (20,21). Second, we developed a hierarchical generalized linear model adjusting for patient characteristics and including a random hospital effect to assess the effect of NIV on the study outcomes.…”
Section: Discussionmentioning
confidence: 99%
“…A multivariable logistic regression model, including all univariate significant predictors of mannitol treatment and the primary outcome, and other clinically important factors (sex and randomized BP-lowering treatment), was constructed to produce estimates of the treatment effect of mannitol (Tables I and II in the online-only Data Supplement). 14,15 On the basis of coefficients from this model, we generated a PS 14,16 for each patient. Only patients with complete data were included in the analyses to maximize balancing of the PS analysis with the largest number of variables and to avoid the need to impute data.…”
Section: Discussionmentioning
confidence: 99%
“…On the basis of coefficients from the multivariable logistic regression model, we generated a PS [7, 8] for each patient. Only patients with complete data were included in the analyses to maximize balancing of the PS analysis with the largest number of variables and to avoid the need to impute data.…”
Section: Methodsmentioning
confidence: 99%