Activity Number:
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480
- Causal Inference and Optimal Decision-Making
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Type:
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Contributed
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Date/Time:
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Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract #323005
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Title:
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Causal Framework for Individualized Treatment Evaluation Using Multivariate Generalized Mixed Effect Models with Longitudinal Data
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Author(s):
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Yizhen Xu* and Aki Nishimura and Jisoo Kim and Brian Garibaldi and Ami Shah and Scott Zeger
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Companies:
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Johns Hopkins University and Johns Hopkins University and Johns Hopkins University and Johns Hopkins University and Johns Hopkins University and Johns Hopkins University
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Keywords:
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Causal Inference;
Mixed Effect Model;
Structural Equation Model;
Longitudinal Treatment Effect
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Abstract:
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Dynamic prediction of causal effects under different treatment regimes conditional on individual's longitudinal history is an essential problem in precision medicine. It is a challenging problem in practice because outcomes and treatment assignment mechanisms are unknown in observational studies; individual's treatment efficacy is a counterfactual; and the existence of selection bias is empirically untestable. We propose a framework for identifying the long-term individualized treatment effect adjusting for unobserved stable trait factors, using Bayesian G-computation with multivariate generalized mixed effect models. Existing methods mostly focus on balancing the confounder distributions of observables between different treatments, while our proposal also accounts for latent tendency towards each treatment due to unobserved time-invariant factors. We assume sequential ignorability conditional on unobserved stable trait factor in treatment assignment, and dynamically update person-specific outcomes progression as history data increases over time. The application is to predict the counterfactual benefit of immediate versus delayed intubation for a hospitalized patient with Covid19.
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Authors who are presenting talks have a * after their name.