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

Background: Randomized controlled trials are often used to inform policy andpractice for broad populations. The average treatment effect (ATE) for a targetpopulation, however, may be different from the ATE observed in a trial if thereare effect modifiers whose distribution in the target population is differentthat from that in the trial. Methods exist to use trial data to estimate thetarget population ATE, provided the distributions of treatment effect modifiersare observed in both the trial and target population -- an assumption that maynot hold in practice.  Methods: The proposed sensitivity analyses address the situation where atreatment effect modifier is observed in the trial but not the targetpopulation. These methods are based on an outcome model or the combination ofsuch a model and weighting adjustment for observed differences between thetrial sample and target population. They accommodate several types of outcomemodels: linear models (including single time outcome and pre- andpost-treatment outcomes) for additive effects, and models with log or logitlink for multiplicative effects. We clarify the methods' assumptions andprovide detailed implementation instructions.  Illustration: We illustrate the methods using an example generalizing theeffects of an HIV treatment regimen from a randomized trial to a relevanttarget population.  Conclusion: These methods allow researchers and decision-makers to have moreappropriate confidence when drawing conclusions about target populationeffects.

plos one

Sensitivity analyses for effect modifiers not observed in the target  population when generalizing treatment effects from a randomized controlled  trial: Assumptions, models, effect scales, data scenarios, and implementation  details