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Causal inference with multistate models—estimands and estimators of the population attributable fraction

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  • Maja von Cube
  • Martin Schumacher
  • Martin Wolkewitz

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 the proportion of preventable cases after an elimination of exposure. Originally, the PAF was 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 the 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.

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  • Maja von Cube & Martin Schumacher & Martin Wolkewitz, 2020. "Causal inference with multistate models—estimands and estimators of the population attributable fraction," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1479-1500, October.
    • Handle: RePEc:bla:jorssa:v:183:y:2020:i:4:p:1479-1500
    DOI: 10.1111/rssa.12486
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