On collapsibility and confounding bias in Cox and Aalen regression models

DOI10.1007/S10985-013-9242-ZzbMath1322.62253OpenAlexW2057684816WikidataQ45248490 ScholiaQ45248490MaRDI QIDQ746469

Stijn Vansteelandt, Torben Martinussen

Publication date: 16 October 2015

Published in: Lifetime Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10985-013-9242-z

zbMATH Keywords

collapsibilityCox modelconfoundingcausal effectAalen's additive model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Estimation in survival analysis and censored data (62N02)

Related Items (11)

Does Cox analysis of a randomized survival study yield a causal treatment effect? ⋮ Instrumental variable estimation of complier causal treatment effect with interval‐censored data ⋮ Simulating longitudinal data from marginal structural models using the additive hazard model ⋮ Expected Precision of Estimation and Probability of Ruling Out a Hypothesis Based on a Confidence Interval ⋮ On the logic of collapsibility for causal effect measures ⋮ Making apples from oranges: Comparing noncollapsible effect estimators and their standard errors after adjustment for different covariate sets ⋮ Sensitivity analysis for observational studies with recurrent events ⋮ Subtleties in the interpretation of hazard contrasts ⋮ Estimation of Causal Odds of Concordance using the Aalen Additive Model ⋮ Estimation of the overall treatment effect in the presence of interference in cluster-randomized trials of infectious disease prevention ⋮ Prevalent cohort studies and unobserved heterogeneity

Uses Software

• timereg

Cites Work

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• Survival and event history analysis. A process point of view
• Confounding and collapsibility in causal inference
• Survival analysis. Techniques for censored and truncated data.
• Dynamic regression models for survival data.
• Attributable fraction functions for censored event times
• Estimation of Direct Effects for Survival Data by using the Aalen Additive Hazards Model
• On quantifying the magnitude of confounding
• Asymptotic Statistics
• A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
• An Individual Measure of Relative Survival

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