Analysis of time-to-event for observational studies: Guidance to the use of intensity models

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Standard

Analysis of time-to-event for observational studies : Guidance to the use of intensity models. / Kragh Andersen, Per; Pohar Perme, Maja; van Houwelingen, Hans C.; Cook, Richard J.; Joly, Pierre; Martinussen, Torben; Taylor, Jeremy M.G.; Abrahamowicz, Michal; Therneau, Terry M.

I: Statistics in Medicine, Bind 40, Nr. 1, 2021, s. 185-211.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Kragh Andersen, P, Pohar Perme, M, van Houwelingen, HC, Cook, RJ, Joly, P, Martinussen, T, Taylor, JMG, Abrahamowicz, M & Therneau, TM 2021, 'Analysis of time-to-event for observational studies: Guidance to the use of intensity models', Statistics in Medicine, bind 40, nr. 1, s. 185-211. https://doi.org/10.1002/sim.8757

APA

Kragh Andersen, P., Pohar Perme, M., van Houwelingen, H. C., Cook, R. J., Joly, P., Martinussen, T., Taylor, J. M. G., Abrahamowicz, M., & Therneau, T. M. (2021). Analysis of time-to-event for observational studies: Guidance to the use of intensity models. Statistics in Medicine, 40(1), 185-211. https://doi.org/10.1002/sim.8757

Vancouver

Kragh Andersen P, Pohar Perme M, van Houwelingen HC, Cook RJ, Joly P, Martinussen T o.a. Analysis of time-to-event for observational studies: Guidance to the use of intensity models. Statistics in Medicine. 2021;40(1):185-211. https://doi.org/10.1002/sim.8757

Author

Kragh Andersen, Per ; Pohar Perme, Maja ; van Houwelingen, Hans C. ; Cook, Richard J. ; Joly, Pierre ; Martinussen, Torben ; Taylor, Jeremy M.G. ; Abrahamowicz, Michal ; Therneau, Terry M. / Analysis of time-to-event for observational studies : Guidance to the use of intensity models. I: Statistics in Medicine. 2021 ; Bind 40, Nr. 1. s. 185-211.

Bibtex

@article{d9202f0a9da245ab841fb3bdfe82e02d,
title = "Analysis of time-to-event for observational studies: Guidance to the use of intensity models",
abstract = "This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time-dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.",
keywords = "censoring, Cox regression model, hazard function, immortal time bias, multistate model, prediction, STRATOS initiative, survival analysis, time-dependent covariates",
author = "{Kragh Andersen}, Per and {Pohar Perme}, Maja and {van Houwelingen}, {Hans C.} and Cook, {Richard J.} and Pierre Joly and Torben Martinussen and Taylor, {Jeremy M.G.} and Michal Abrahamowicz and Therneau, {Terry M.}",
year = "2021",
doi = "10.1002/sim.8757",
language = "English",
volume = "40",
pages = "185--211",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "JohnWiley & Sons Ltd",
number = "1",

}

RIS

TY - JOUR

T1 - Analysis of time-to-event for observational studies

T2 - Guidance to the use of intensity models

AU - Kragh Andersen, Per

AU - Pohar Perme, Maja

AU - van Houwelingen, Hans C.

AU - Cook, Richard J.

AU - Joly, Pierre

AU - Martinussen, Torben

AU - Taylor, Jeremy M.G.

AU - Abrahamowicz, Michal

AU - Therneau, Terry M.

PY - 2021

Y1 - 2021

N2 - This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time-dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.

AB - This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time-dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.

KW - censoring

KW - Cox regression model

KW - hazard function

KW - immortal time bias

KW - multistate model

KW - prediction

KW - STRATOS initiative

KW - survival analysis

KW - time-dependent covariates

U2 - 10.1002/sim.8757

DO - 10.1002/sim.8757

M3 - Journal article

C2 - 33043497

AN - SCOPUS:85092360284

VL - 40

SP - 185

EP - 211

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 1

ER -

ID: 250205555