Event History Analysis in Continuous Time

Publikation: Bidrag til bog/antologi/rapportEncyclopædiartikelForskningfagfællebedømt

Standard

Event History Analysis in Continuous Time. / Andersen, Per K.; Keiding, Niels.

International Encyclopedia of the Social & Behavioral Sciences. red. / James D. Wright. 2. udg. Elsevier Science Inc., 2015. s. 310-319.

Publikation: Bidrag til bog/antologi/rapportEncyclopædiartikelForskningfagfællebedømt

Harvard

Andersen, PK & Keiding, N 2015, Event History Analysis in Continuous Time. i JD Wright (red.), International Encyclopedia of the Social & Behavioral Sciences. 2 udg, Elsevier Science Inc., s. 310-319. https://doi.org/10.1016/B978-0-08-097086-8.31131-X

APA

Andersen, P. K., & Keiding, N. (2015). Event History Analysis in Continuous Time. I J. D. Wright (red.), International Encyclopedia of the Social & Behavioral Sciences (2 udg., s. 310-319). Elsevier Science Inc.. https://doi.org/10.1016/B978-0-08-097086-8.31131-X

Vancouver

Andersen PK, Keiding N. Event History Analysis in Continuous Time. I Wright JD, red., International Encyclopedia of the Social & Behavioral Sciences. 2 udg. Elsevier Science Inc. 2015. s. 310-319 https://doi.org/10.1016/B978-0-08-097086-8.31131-X

Author

Andersen, Per K. ; Keiding, Niels. / Event History Analysis in Continuous Time. International Encyclopedia of the Social & Behavioral Sciences. red. / James D. Wright. 2. udg. Elsevier Science Inc., 2015. s. 310-319

Bibtex

@inbook{b314631eb8ce459da8631a3ce6fdefd9,
title = "Event History Analysis in Continuous Time",
abstract = "Event history data are obtained by observing individuals over time, focusing on times of occurrence of certain events and the types of event occurring. A review is given of event history analysis (in continuous time) based on multistate models. Examples of such models include the two-state model for survival data and the competing risks and disability models. The likelihood function for a multistate model is presented using the theory of counting processes with special emphasis on models with piecewise constant transition intensities, nonparametric models, and models for the transition intensities including covariates along the lines of the semiparametric Cox proportional hazards regression model for survival data. Direct models for various marginal features of event history data, such as state occupation probabilities, are also discussed. Finally, the influence of observational patterns on the inference is discussed.",
keywords = "Censored data, Competing risks, Counting process, Cox regression model, Event history analysis, Markov process, Multistate model, Piecewise constant intensities, State occupation probability, Survival analysis, Time-dependent covariate, Transition intensity, Transition probability",
author = "Andersen, {Per K.} and Niels Keiding",
year = "2015",
doi = "10.1016/B978-0-08-097086-8.31131-X",
language = "English",
isbn = "9780080970868",
pages = "310--319",
editor = "{ Wright}, {James D.}",
booktitle = "International Encyclopedia of the Social & Behavioral Sciences",
publisher = "Elsevier Science Inc.",
address = "United States",
edition = "2",

}

RIS

TY - ENCYC

T1 - Event History Analysis in Continuous Time

AU - Andersen, Per K.

AU - Keiding, Niels

PY - 2015

Y1 - 2015

N2 - Event history data are obtained by observing individuals over time, focusing on times of occurrence of certain events and the types of event occurring. A review is given of event history analysis (in continuous time) based on multistate models. Examples of such models include the two-state model for survival data and the competing risks and disability models. The likelihood function for a multistate model is presented using the theory of counting processes with special emphasis on models with piecewise constant transition intensities, nonparametric models, and models for the transition intensities including covariates along the lines of the semiparametric Cox proportional hazards regression model for survival data. Direct models for various marginal features of event history data, such as state occupation probabilities, are also discussed. Finally, the influence of observational patterns on the inference is discussed.

AB - Event history data are obtained by observing individuals over time, focusing on times of occurrence of certain events and the types of event occurring. A review is given of event history analysis (in continuous time) based on multistate models. Examples of such models include the two-state model for survival data and the competing risks and disability models. The likelihood function for a multistate model is presented using the theory of counting processes with special emphasis on models with piecewise constant transition intensities, nonparametric models, and models for the transition intensities including covariates along the lines of the semiparametric Cox proportional hazards regression model for survival data. Direct models for various marginal features of event history data, such as state occupation probabilities, are also discussed. Finally, the influence of observational patterns on the inference is discussed.

KW - Censored data

KW - Competing risks

KW - Counting process

KW - Cox regression model

KW - Event history analysis

KW - Markov process

KW - Multistate model

KW - Piecewise constant intensities

KW - State occupation probability

KW - Survival analysis

KW - Time-dependent covariate

KW - Transition intensity

KW - Transition probability

U2 - 10.1016/B978-0-08-097086-8.31131-X

DO - 10.1016/B978-0-08-097086-8.31131-X

M3 - Encyclopedia chapter

AN - SCOPUS:85043432443

SN - 9780080970868

SP - 310

EP - 319

BT - International Encyclopedia of the Social & Behavioral Sciences

A2 - Wright, James D.

PB - Elsevier Science Inc.

ER -

ID: 201449092