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/

Zendy Plus

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

Zendy Tools

Zendy Open

Randomized clinical trials are considered the gold standard for estimatingcausal effects. Nevertheless, in studies that are aimed at examining adverseeffects of interventions, such trials are often impractical because of ethicaland financial considerations. In observational studies, matching on thegeneralized propensity scores was proposed as a possible solution to estimatethe treatment effects of multiple interventions. However, the derivation ofpoint and interval estimates for these matching procedures can become complexwith non-continuous or censored outcomes. We propose a novel ApproximateBayesian Bootstrap algorithm that result in statistically valid point andinterval estimates of the treatment effects with categorical outcomes. Theprocedure relies on the estimated generalized propensity scores and multiplyimputes the unobserved potential outcomes for each unit. In addition, wedescribe a corresponding interpretable sensitivity analysis to examine theunconfoundedness assumption. We apply this approach to examines thecardiovascular safety of common, real-world anti-diabetic treatment regimensfor Type 2 diabetes mellitus in a large observational database.

Approximate Bayesian Bootstrap Procedures to Estimate Multilevel  Treatment Effects in Observational Studies with Application to Type 2  Diabetes Treatment Regimens