Activity Number:
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552
- JASA, AandC Invited Session
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Type:
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Invited
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Date/Time:
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Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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JASA, Applications and Case Studies
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Abstract #300259
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Presentation
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Title:
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Penalized Spline of Propensity
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Author(s):
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Roderick J Little* and Tingting Zhou and Michael Elliott
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Companies:
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University of Michigan School of Public Health and University of Michigan School of Public Health and University of Michigan
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Keywords:
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causal inference;
double robustness;
time-dependent confounders;
Rubin causal model
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Abstract:
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Valid causal inference from observational studies requires controlling for confounders. When time-dependent confounders serve as mediators of treatment effects and affect future treatment assignment, standard regression methods for controlling for confounders fail. We propose a robust Bayesian approach to causal inference in this setting called Penalized Spline of Propensity Methods for Treatment Comparison (PENCOMP), which builds on the Penalized Spline of Propensity Prediction method for missing data problems. PENCOMP estimates causal effects by imputing missing potential outcomes with flexible spline models, and draws inference based on imputed and observed outcomes. PENCOMP has a double robustness property for causal effects, and simulations suggest that it tends to outperform doubly-robust marginal structural modeling when the weights are variable. We applied our method to the Multicenter AIDS Cohort study (MACS) to estimate the effect of antiretroviral treatment on CD4 counts in HIV infected cases. Approaches to address imbalance in propensity score distributions between groups are also discussed.
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Authors who are presenting talks have a * after their name.