Asymptotic properties of resampling-based processes for the average treatment effect in observational studies with competing risks
Statistics Theory
2024-04-26 v1 Statistics Theory
Abstract
In observational studies with time-to-event outcomes, the g-formula can be used to estimate a treatment effect in the presence of confounding factors. However, the asymptotic distribution of the corresponding stochastic process is complicated and thus not suitable for deriving confidence intervals or time-simultaneous confidence bands for the average treatment effect. A common remedy are resampling-based approximations, with Efron's nonparametric bootstrap being the standard tool in practice. We investigate the large sample properties of three different resampling approaches and prove their asymptotic validity in a setting with time-to-event data subject to competing risks.
Keywords
Cite
@article{arxiv.2306.02970,
title = {Asymptotic properties of resampling-based processes for the average treatment effect in observational studies with competing risks},
author = {Jasmin Rühl and Sarah Friedrich},
journal= {arXiv preprint arXiv:2306.02970},
year = {2024}
}