The foundations of confounding in epidemiology

DOI10.1016/0898-1221(87)90236-7zbMath0647.62093OpenAlexW2078225583MaRDI QIDQ1104676

H. Morgenstern, James M. Robins

Publication date: 1987

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0898-1221(87)90236-7

zbMATH Keywords

epidemiologyconfoundingcovariateabsence of datanonconfounderpresence of data

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Population dynamics (general) (92D25)

Related Items

Matching methods for causal inference: a review and a look forward ⋮ Identification, inference and sensitivity analysis for causal mediation effects ⋮ Graphical models for inference under outcome-dependent sampling ⋮ Identifying the consequences of dynamic treatment strategies: a decision-theoretic overview ⋮ On the definition of a confounder ⋮ Defining and estimating intervention effects for groups that will develop an auxiliary outcome ⋮ Confounding and collapsibility in causal inference ⋮ Statistical modeling of causal effects in continuous time ⋮ Semiparametric theory for causal mediation analysis: efficiency bounds, multiple robustness and sensitivity analysis ⋮ Causal inference in statistics: an overview ⋮ Criteria for Confounders in Epidemiological Studies ⋮ Conditions for Non‐confounding and Collapsibility without Knowledge of Completely Constructed Causal Diagrams ⋮ Related Causal Frameworks for Surrogate Outcomes ⋮ Some Results About Standardization for a Non Confounder in Estimators of (log) Relative Risk

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Asymptotic properties of maximum likelihood estimators based on conditional specification
• Bootstrap methods: another look at the jackknife
• Conditionality resolutions
• Nuisance parameters, mixture models, and the efficiency of partial likelihood estimators
• Odds ratio estimators when the data are sparse
• Geometrical theory of asymptotic ancillarity and conditional inference
• Some Ancillary Statistics and Their Properties
• Nonformation
• Retrospective studies and failure time models
• Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information
• A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
• Two new properties of mathematical likelihood

This page was built for publication: The foundations of confounding in epidemiology