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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

It has recently become popular to define treatment effects for subsets of thetarget population characterized by variables not observable at the time atreatment decision is made. Characterizing and estimating such treatmenteffects is tricky; the most popular but naive approach inappropriately adjustsfor variables affected by treatment and so is biased. We consider severalappropriate ways to formalize the effects: principal stratification,stratification on a single potential auxiliary variable, stratification on anobserved auxiliary variable and stratification on expected levels of auxiliaryvariables. We then outline identifying assumptions for each type of estimand.We evaluate the utility of these estimands and estimation procedures fordecision making and understanding causal processes, contrasting them with theconcepts of direct and indirect effects. We motivate our development withexamples from nephrology and cancer screening, and use simulated data and realdata on cancer screening to illustrate the estimation methods.

Defining and Estimating Intervention Effects for Groups that will Develop an Auxiliary Outcome