Affiliation:
1. Mathematical Statistics and Artificial Intelligence in Medicine Augsburg University Augsburg Germany
2. Centre for Advanced Analytics and Predictive Sciences (CAAPS) Augsburg University Augsburg Germany
Abstract
AbstractIn 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. The usage of these approaches is demonstrated by an analysis of the effect of physical activity on the risk of knee replacement among patients with advanced knee osteoarthritis.
Funder
Deutsche Forschungsgemeinschaft
Cited by
1 articles.
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