Identification, estimation and approximation of risk under interventions that depend on the natural value of treatment using observational data

DOI10.1515/em-2012-0001zbMath1343.92252OpenAlexW2086288483WikidataQ30930256 ScholiaQ30930256MaRDI QIDQ306793

Jessica G. Young, Miguel A. Hernán, James M. Robins

Publication date: 1 September 2016

Published in: Epidemiologic Methods (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc4387917

zbMATH Keywords

survival analysiscausal inferenceg-formulainverse-probability weightingrandom dynamic regimes

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Estimation in survival analysis and censored data (62N02) Testing in survival analysis and censored data (62N03)

Related Items (6)

Parametric g‐formula implementations for causal survival analyses ⋮ Causal Inference for Social Network Data ⋮ Causal survival analysis under competing risks using longitudinal modified treatment policies ⋮ Nonparametric Causal Effects Based on Longitudinal Modified Treatment Policies ⋮ Nonparametric Causal Effects Based on Incremental Propensity Score Interventions ⋮ Inverse Probability Weighted Estimation of Risk Under Representative Interventions in Observational Studies

Uses Software

• TETRAD

Cites Work

• Unnamed Item
• Unnamed Item
• Population Intervention Causal Effects Based on Stochastic Interventions
• Causation, prediction, and search
• Identifying the consequences of dynamic treatment strategies: a decision-theoretic overview
• History-Adjusted Marginal Structural Models and Statically-Optimal Dynamic Treatment Regimens
• Marginal Mean Models for Dynamic Regimes
• Structural Nested Cumulative Failure Time Models to Estimate the Effects of Interventions
• A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect

This page was built for publication: Identification, estimation and approximation of risk under interventions that depend on the natural value of treatment using observational data