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

Here, we explain and illustrate a geometric perspective on causal inferencein cohort studies that can help epidemiologists understand the role ofstandardization in causal inference as well as the distinctions betweenconfounding, effect modification, and noncollapsibility. For simplicity, wefocus on a binary exposure X, a binary outcome D, and a binary confounder Cthat is not causally affected by X. Rothman diagrams plot risk in the unexposedon the x-axis and risk in the exposed on the y-axis. The crude risks define onepoint in the unit square, and the stratum-specific risks define two otherpoints in the unit square. These three points can be used to identifyconfounding and effect modification, and we show briefly how these conceptsgeneralize to confounders with more than two levels. We propose a simplifiedbut equivalent definition of collapsibility in terms of standardization, and weshow that a measure of association is collapsible if and only if all of itscontour lines are straight. We illustrate these ideas using data from a studyconducted in Newcastle upon Tyne, United Kingdom, where the causal effect ofsmoking on 20-year mortality was confounded by age. We conclude that causalinference should be taught using geometry before using regression models.

Rothman diagrams: the geometry of causal inference in epidemiology