Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: G-computation and doubly robust standardisation for continuous-time
  data: a comparison with inverse probability weighting   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.

22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph