Nonparametric Estimation of the Potential Impact Fraction and Population Attributable Fraction with Individual-Level and Aggregated Data

TL;DR

This paper introduces nonparametric methods for estimating the potential impact fraction and population attributable fraction using individual-level or aggregated data, addressing biases from distributional assumptions in cross-sectional studies.

Contribution

It develops novel nonparametric estimation techniques for impact fractions applicable to cross-sectional data, filling a gap in existing methods that are limited to cohort data.

Findings

Nonparametric methods outperform parametric ones under distributional violations.

Simulation studies demonstrate improved bias and accuracy.

Application to sugar-sweetened beverage data illustrates practical utility.

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 incidence of type 2 diabetes. We also present an R package pifpaf to implement these methods.