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Analysis of time‐to‐event for observational studies: Guidance to the use of intensity models
Author(s) -
Kragh Andersen Per,
Pohar Perme Maja,
Houwelingen Hans C.,
Cook Richard J.,
Joly Pierre,
Martinussen Torben,
Taylor Jeremy M. G.,
Abrahamowicz Michal,
Therneau Terry M.
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8757
Subject(s) - censoring (clinical trials) , computer science , covariate , proportional hazards model , hazard , event (particle physics) , observational study , econometrics , regression analysis , goodness of fit , statistics , data mining , machine learning , mathematics , chemistry , physics , organic chemistry , quantum mechanics
Summary 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.