  In time-to-event settings, g-computation and doubly robust estimators are
based on discrete-time data. However, many biological processes are evolving
continuously over time. In this paper, we extend the g-computation and the
doubly robust standardisation procedures to a continuous-time context. We
compare their performance to the well-known inverse-probability-weighting (IPW)
estimator for the estimation of the hazard ratio and restricted mean survival
times difference, using a simulation study. Under a correct model
specification, all methods are unbiased, but g-computation and the doubly
robust standardisation are more efficient than inverse probability weighting.
We also analyse two real-world datasets to illustrate the practical
implementation of these approaches. We have updated the R package RISCA to
facilitate the use of these methods and their dissemination.
