Monte-Carlo Sensitivity Analysis for Controlled Direct Effects Using Marginal Structural Models in the Presence of Confounded Mediators

DOI10.1080/03610926.2010.551016zbMath1271.62252OpenAlexW1969001486MaRDI QIDQ2920002

Yasutaka Chiba

Publication date: 23 October 2012

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2010.551016

zbMATH Keywords

causal inferencepotential outcomerandomized trialsinverse-probability-of-treatment-weighting

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Monte Carlo methods (65C05) Medical applications (general) (92C50)

Uses Software

• SAS/STAT

Cites Work

• Bayesian inference for causal effects: The role of randomization
• Association, causation, and marginal structural models.
• Bounds on controlled direct effects under monotonic assumptions about mediators and confounders
• Multiple-Bias Modelling for Analysis of Observational Data
• The central role of the propensity score in observational studies for causal effects
• Causal diagrams for empirical research
• The Impact of Prior Distributions for Uncontrolled Confounding and Response Bias
• Sensitivity Analysis of Unmeasured Confounding for the Causal Risk Ratio by Applying Marginal Structural Models
• Causal Mediation Analyses with Rank Preserving Models

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