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Activity Number: 487 - Novel Causal Inference Methods for Epidemiology Studies
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #322054
Title: Nonparametric Estimation of the Potential Impact Fraction and the Population Attributable Fraction
Author(s): Colleen Elise Chan* and Rodrigo Zepeda-Tello and Dalia Camacho-García-Formentí and Frederick Cudhea and Rafael Meza and Eliane Rodrigues and Donna Spiegelman and Tonatiuh Barrientos-Gutiérrez and Xin Zhou
Companies: Yale University and National Institute of Public Health of Mexico and National Institute of Public Health of Mexico and Tufts University and University of Michigan School of Public Health and Universidad Nacional Autónoma de México and Yale School of Public Health and National Institute of Public Health of Mexico and Yale School of Public Health
Keywords: Epidemiologic methods; Nonparametric methods; Potential impact fraction; Population attributable fraction
Abstract:

The estimation of the potential impact fraction (including the population attributable fraction) with continuous exposure data frequently relies on strong distributional assumptions. However, these assumptions are often violated if the underlying exposure distribution is unknown or if the same distribution is assumed across time or space. Nonparametric methods to estimate the potential impact fraction are available for cohort data, but no alternatives exist for cross-sectional data. In this article, we discuss the impact of distributional assumptions in the estimation of the population impact fraction, showing that under an infinite set of possibilities, distributional violations lead to biased estimates. We propose nonparametric methods to estimate the potential impact fraction for aggregated (mean and standard deviation) or individual data (e.g. observations from a cross-sectional population survey), and develop simulation scenarios to compare their performance against standard parametric procedures. We illustrate our methodology on an application of sugar-sweetened beverage consumption on type 2 diabetes incidence. We also present an R package pifpaf to implement these methods.


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