Causal inference with multi-state models - estimands and estimators of the population-attributable fraction
Abstract
The population-attributable fraction (PAF) is a popular epidemiological measure for the burden of a harmful exposure within a population. It is often interpreted causally as proportion of preventable cases after an elimination of exposure. Originally, the PAF has been defined for cohort studies of fixed length with a baseline exposure or cross-sectional studies. An extension of the definition to complex time-to-event data is not straightforward. We revise the proposed approaches in literature and provide a clear concept of the PAF for these data situations. The conceptualization is achieved by a proper differentiation between estimands and estimators as well as causal effect measures and measures of association.
Keywords
Cite
@article{arxiv.1903.10315,
title = {Causal inference with multi-state models - estimands and estimators of the population-attributable fraction},
author = {Maja von Cube and Martin Schumacher and Martin Wolkewitz},
journal= {arXiv preprint arXiv:1903.10315},
year = {2019}
}
Comments
A revised version of this manuscript has been submitted to a journal on March 8 2019