scholarly journals Causal Inference with Multilevel Data: A Comparison of Different Propensity Score Weighting Approaches

2021 ◽  
pp. 1-24
Author(s):  
Alvaro Fuentes ◽  
Oliver Lüdtke ◽  
Alexander Robitzsch
Keyword(s):  
Propensity Score ◽  
Causal Inference ◽  
Multilevel Data ◽  
Propensity Score Weighting

scholarly journals Propensity Score Weighting for Causal Inference with Clustered Data

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Shu Yang
Keyword(s):  
Propensity Score ◽  
Causal Inference ◽  
Clustered Data ◽  
Overweight And Obesity ◽  
Average Treatment Effect ◽  
Treatment Assignment ◽  
Propensity Score Model ◽  
Propensity Score Weighting ◽  
Score Model ◽  
Cluster Level

AbstractPropensity score weighting is a tool for causal inference to adjust for measured confounders in observational studies. In practice, data often present complex structures, such as clustering, which make propensity score modeling and estimation challenging. In addition, for clustered data, there may be unmeasured cluster-level covariates that are related to both the treatment assignment and outcome. When such unmeasured cluster-specific confounders exist and are omitted in the propensity score model, the subsequent propensity score adjustment may be biased. In this article, we propose a calibration technique for propensity score estimation under the latent ignorable treatment assignment mechanism, i. e., the treatment-outcome relationship is unconfounded given the observed covariates and the latent cluster-specific confounders. We impose novel balance constraints which imply exact balance of the observed confounders and the unobserved cluster-level confounders between the treatment groups. We show that the proposed calibrated propensity score weighting estimator is doubly robust in that it is consistent for the average treatment effect if either the propensity score model is correctly specified or the outcome follows a linear mixed effects model. Moreover, the proposed weighting method can be combined with sampling weights for an integrated solution to handle confounding and sampling designs for causal inference with clustered survey data. In simulation studies, we show that the proposed estimator is superior to other competitors. We estimate the effect of School Body Mass Index Screening on prevalence of overweight and obesity for elementary schools in Pennsylvania.

scholarly journals Propensity score weighting with multilevel data

2013 ◽  
Vol 32 (19) ◽  
pp. 3373-3387 ◽  
Author(s):  
Fan Li ◽  
Alan M. Zaslavsky ◽  
Mary Beth Landrum
Keyword(s):  
Propensity Score ◽  
Multilevel Data ◽  
Propensity Score Weighting

scholarly journals Estimating the causal effects of modifiable, non-genetic factors on Huntington disease progression using propensity score weighting

2021 ◽  
Vol 83 ◽  
pp. 56-62
Author(s):  
Beth Ann Griffin ◽  
Marika Suttorp Booth ◽  
Monica Busse ◽  
Edward J. Wild ◽  
Claude Setodji ◽  
...  
Keyword(s):  
Propensity Score ◽  
Disease Progression ◽  
Huntington Disease ◽  
Genetic Factors ◽  
Causal Effects ◽  
Propensity Score Weighting

Association of Body Mass Index With Clinical Outcomes in Patients With Heart Failure With Reduced Ejection Fraction Treated With Sacubitril/Valsartan

2021 ◽  
pp. 107424842110244
Author(s):  
Kazuhiko Kido ◽  
Christopher Bianco ◽  
Marco Caccamo ◽  
Wei Fang ◽  
George Sokos
Keyword(s):  
Heart Failure ◽  
Body Mass Index ◽  
Propensity Score ◽  
Ejection Fraction ◽  
Clinical Outcomes ◽  
Body Mass ◽  
Normal Weight ◽  
Reduced Ejection Fraction ◽  
Propensity Score Weighting ◽  
Patients With Heart Failure

Background: Only limited data are available that address the association between body mass index (BMI) and clinical outcomes in patients with heart failure with reduced ejection fraction who are receiving sacubitril/valsartan. Methods: We performed a retrospective multi-center cohort study in which we compared 3 body mass index groups (normal, overweight and obese groups) in patients with heart failure with reduced ejection fraction receiving sacubitril/valsartan. The follow-up period was at least 1 year. Propensity score weighting was performed. The primary outcomes were hospitalization for heart failure and all-cause mortality. Results: Of the 721 patients in the original cohort, propensity score weighting generated a cohort of 540 patients in 3 groups: normal weight (n = 78), overweight (n = 181), and obese (n = 281). All baseline characteristics were well-balanced between 3 groups after propensity score weighting. Among our results, we found no significant differences in hospitalization for heart failure (normal weight versus overweight: average hazard ratio [AHR] 1.29, 95% confidence interval [CI] = 0.76-2.20, P = 0.35; normal weight versus obese: AHR 1.04, 95% CI = 0.63-1.70, P = 0.88; overweight versus obese groups: AHR 0.81, 95% CI = 0.54-1.20, P = 0.29) or all-cause mortality (normal weight versus overweight: AHR 0.99, 95% CI = 0.59-1.67, P = 0.97; normal weight versus obese: AHR 0.87, 95% CI = 0.53-1.42, P = 0.57; overweight versus obese: AHR 0.87, 95% CI = 0.58-1.32, P = 0.52). Conclusion: We identified no significant associations between BMI and clinical outcomes in patients diagnosed with heart failure with a reduced ejection fraction who were treated with sacubitril/valsartan. A large-scale study should be performed to verify these results.

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