Analysis of time-to-event for observational studies: Guidance to the use of intensity models
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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.
In: Statistics in Medicine, Vol. 40, No. 1, 2021, p. 185-211.Research output: Contribution to journal › Journal article › Research › peer-review
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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