Dynamic path analysis -- a new approach to analyzing time-dependent covariates

DOI10.1007/s10985-006-9004-2zbMath1134.62367OpenAlexW2094417738WikidataQ79830139 ScholiaQ79830139MaRDI QIDQ849902

Johan Fosen, Ørnulf Borgan, Odd O. Aalen, Egil Ferkingstad

Publication date: 15 November 2006

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

Full work available at URL: https://doi.org/10.1007/s10985-006-9004-2

zbMATH Keywords

survival analysistime-dependent covariatescounting processesgraphical modelsdirect effectevent history analysisindirect effecttotal effectadditive regression modelinternal covariates

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Survival analysis and censored data (62N99) Inference from stochastic processes (62M99)

Related Items (14)

Unnamed Item ⋮ Feedback and Mediation in Causal Inference Illustrated by Stochastic Process Models ⋮ A dynamic approach for reconstructing missing longitudinal data using the linear increments model ⋮ Integrative genomics with mediation analysis in a survival context ⋮ What can Statistics Contribute to a Causal Understanding? ⋮ Dealing with death when studying disease or physiological marker: the stochastic system approach to causality ⋮ A general definition of influence between stochastic processes ⋮ Dynamic path analysis for event time data: large sample properties and inference ⋮ Longitudinal perspectives on event history analysis ⋮ Evidence synthesis through a degradation model applied to myocardial infarction ⋮ Special issue dedicated to Odd O. Aalen ⋮ Defining causal mediation with a longitudinal mediator and a survival outcome ⋮ Estimation of Direct Effects for Survival Data by using the Aalen Additive Hazards Model ⋮ Interview with Anja Sattelmacher: between viewing and touching -- models and their materiality

Uses Software

• ElemStatLearn
• addreg
• MIM

Cites Work

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• Unnamed Item
• Dynamic modelling and causality
• Dynamic Analysis of Multivariate Failure Time Data
• Robust Locally Weighted Regression and Smoothing Scatterplots
• Censored Data and the Bootstrap
• Introduction to Graphical Modelling
• The Method of Path Coefficients
• Attenuation caused by infrequently updated covariates in survival analysis
• Statistical models based on counting processes
• The elements of statistical learning. Data mining, inference, and prediction

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