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

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 availableRsoftware are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.

Automated summary

This paper offers guidance for researchers with mathematical background on conducting time-to-event analysis based on intensity models in observational studies. It covers basic concepts like time axis, event definition, and censoring, introduces hazard models with emphasis on the Cox proportional hazards regression model, provides check lists for fitting and assessing the model's goodness of fit, and discusses avoiding immortal time bias and prediction challenges. Examples and implementation details using R software are also included.