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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Population attributable fractions aim to quantify the proportion of the casesof an outcome (for example, a disease) that would have been avoided had noindividuals in the population been exposed to a given exposure. This quantitythus plays a crucial role in epidemiology and public health, notably to guidepolicies, interventions or to assess the burden of a disease due to aparticular exposure. Various statistical methods have been proposed to estimateattributable fractions using observational data. When time-to-event data areused, several of these formulas yield invalid results. Alternative validformulas are available but remain scarcely used. We propose a new estimator ofthe attributable fraction that is both conceptually simple and easy toimplement using common statistical software. Our proposed estimator makes useof the Kaplan-Meier estimator to address censoring and potentiallynon-proportional hazards, as well as inverse probability weighting to controlconfounding. Nonparametric bootstrap is proposed to produce inferences. Asimulation study is used to illustrate and compare our proposed estimator toseveral alternatives. The results showcase the bias of many commonly usedtraditional approaches and the validity of our estimator under its workingassumptions.

Estimation of the attributable fraction for time to event outcomes using an inverse probability of exposure weighted Kaplan-Meier estimator