English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The estimation of the potential impact fraction (including the populationattributable fraction) with continuous exposure data frequently relies onstrong distributional assumptions. However, these assumptions are oftenviolated if the underlying exposure distribution is unknown or if the samedistribution is assumed across time or space. Nonparametric methods to estimatethe potential impact fraction are available for cohort data, but noalternatives exist for cross-sectional data. In this article, we discuss theimpact of distributional assumptions in the estimation of the population impactfraction, showing that under an infinite set of possibilities, distributionalviolations lead to biased estimates. We propose nonparametric methods toestimate the potential impact fraction for aggregated (mean and standarddeviation) or individual data (e.g. observations from a cross-sectionalpopulation survey), and develop simulation scenarios to compare theirperformance against standard parametric procedures. We illustrate ourmethodology on an application of sugar-sweetened beverage consumption onincidence of type 2 diabetes. We also present an R package pifpaf to implementthese methods.

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