Primary Submission Category: Causal Inference and Bias/Discrimination
Attributable fraction and related measures: conceptual relations in the counterfactual framework
Authors: Etsuji Suzuki, Eiji Yamamoto,
Presenting Author: Etsuji Suzuki*
The attributable fraction (population) has attracted much attention from a theoretical perspective and has been used extensively to assess the impact of potential health interventions. However, despite its extensive use, there is much confusion about its concept and calculation methods. In this presentation, we discuss the concepts of and calculation methods for the attributable fraction and related measures in the counterfactual framework, both with and without stratification by covariates. Generally, the attributable fraction is useful when the exposure of interest has a causal effect on the outcome. However, it is important to understand that this statement applies to the exposed group. Although the target population of the attributable fraction (population) is the total population, the causal effect should be present not in the total population but in the exposed group. As related measures, we discuss the preventable fraction and prevented fraction, which are generally useful when the exposure of interest has a preventive effect on the outcome, and we further propose a new measure called the attributed fraction. We also discuss the causal and preventive excess fractions. Finally, we discuss the relations between the aforementioned six measures and six possible patterns using a conceptual schema. It is important to have clear definitions of them in the counterfactual framework, which would improve the interpretation and use of these measures.