scholarly journals Doubly robust matching estimators for high dimensional confounding adjustment

Biometrics ◽  
2018 ◽  
Vol 74 (4) ◽  
pp. 1171-1179 ◽  
Author(s):  
Joseph Antonelli ◽  
Matthew Cefalu ◽  
Nathan Palmer ◽  
Denis Agniel
Keyword(s):  
High Dimensional ◽  
Matching Estimators ◽  
Doubly Robust ◽  
Confounding Adjustment

scholarly journals Doubly Robust Estimation of Causal Effect

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Xiaochun Li ◽  
Changyu Shen
Keyword(s):  
Propensity Score ◽  
Causal Effect ◽  
Model Specification ◽  
Robust Estimator ◽  
Data Set ◽  
Multiple Regressions ◽  
Doubly Robust Estimation ◽  
Doubly Robust ◽  
Confounding Adjustment ◽  
The Relationship

Propensity score–based methods or multiple regressions of the outcome are often used for confounding adjustment in analysis of observational studies. In either approach, a model is needed: A model describing the relationship between the treatment assignment and covariates in the propensity score–based method or a model for the outcome and covariates in the multiple regressions. The 2 models are usually unknown to the investigators and must be estimated. The correct model specification, therefore, is essential for the validity of the final causal estimate. We describe in this article a doubly robust estimator which combines both models propitiously to offer analysts 2 chances for obtaining a valid causal estimate and demonstrate its use through a data set from the Lindner Center Study.

Variable Selection for Confounding Adjustment in High-dimensional Covariate Spaces When Analyzing Healthcare Databases

Epidemiology ◽  
2017 ◽  
Vol 28 (2) ◽  
pp. 237-248 ◽  
Author(s):  
Sebastian Schneeweiss ◽  
Wesley Eddings ◽  
Robert J. Glynn ◽  
Elisabetta Patorno ◽  
Jeremy Rassen ◽  
...  
Keyword(s):  
Variable Selection ◽  
High Dimensional ◽  
Selection For ◽  
Confounding Adjustment

scholarly journals High-Dimensional Confounding Adjustment Using Continuous Spike and Slab Priors

2019 ◽  
Vol 14 (3) ◽  
pp. 805-828 ◽  
Author(s):  
Joseph Antonelli ◽  
Giovanni Parmigiani ◽  
Francesca Dominici
Keyword(s):  
High Dimensional ◽  
Confounding Adjustment

scholarly journals Doubly robust tests of exposure effects under high‐dimensional confounding

Biometrics ◽  
2020 ◽  
Vol 76 (4) ◽  
pp. 1190-1200 ◽  
Author(s):  
Oliver Dukes ◽  
Vahe Avagyan ◽  
Stijn Vansteelandt
Keyword(s):  
High Dimensional ◽  
Robust Tests ◽  
Doubly Robust

scholarly journals A comparison of confounding adjustment methods with an application to early life determinants of childhood obesity

2014 ◽  
Vol 5 (6) ◽  
pp. 435-447 ◽  
Author(s):  
L. Li ◽  
K. Kleinman ◽  
M. W. Gillman
Keyword(s):  
Cesarean Section ◽  
Inverse Probability Weighting ◽  
The Body ◽  
Birth Cohort Study ◽  
Probability Weighting ◽  
Regression Estimate ◽  
Inverse Probability ◽  
Doubly Robust Estimation ◽  
Doubly Robust ◽  
Confounding Adjustment

We implemented six confounding adjustment methods: (1) covariate-adjusted regression, (2) propensity score (PS) regression, (3) PS stratification, (4) PS matching with two calipers, (5) inverse probability weighting and (6) doubly robust estimation to examine the associations between the body mass index (BMI) z-score at 3 years and two separate dichotomous exposure measures: exclusive breastfeeding v. formula only (n=437) and cesarean section v. vaginal delivery (n=1236). Data were drawn from a prospective pre-birth cohort study, Project Viva. The goal is to demonstrate the necessity and usefulness, and approaches for multiple confounding adjustment methods to analyze observational data. Unadjusted (univariate) and covariate-adjusted linear regression associations of breastfeeding with BMI z-score were −0.33 (95% CI −0.53, −0.13) and −0.24 (−0.46, −0.02), respectively. The other approaches resulted in smaller n (204–276) because of poor overlap of covariates, but CIs were of similar width except for inverse probability weighting (75% wider) and PS matching with a wider caliper (76% wider). Point estimates ranged widely, however, from −0.01 to −0.38. For cesarean section, because of better covariate overlap, the covariate-adjusted regression estimate (0.20) was remarkably robust to all adjustment methods, and the widths of the 95% CIs differed less than in the breastfeeding example. Choice of covariate adjustment method can matter. Lack of overlap in covariate structure between exposed and unexposed participants in observational studies can lead to erroneous covariate-adjusted estimates and confidence intervals. We recommend inspecting covariate overlap and using multiple confounding adjustment methods. Similar results bring reassurance. Contradictory results suggest issues with either the data or the analytic method.

Sign in / Sign up

Export Citation Format

Share Document