Analysis of time‐to‐event for observational studies: Guidance to the use of intensity models. (11th October 2020)

• Record Type: Journal Article
• Authors: 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.
• Is Part Of: Statistics in medicine. Volume 40:Number 1(2021)
• Page Start: 185
• Page End: 211
• Publication Date: 2020-10-11
• Subjects: censoring -- Cox regression model -- hazard function -- immortal time bias -- multistate model -- prediction -- STRATOS initiative -- survival analysis -- time‐dependent covariates; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8757
• Languages: English
• ISSNs: 0277-6715

Abstract

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.