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

Methods utilizing instrumental variables have been a fundamental statisticalapproach to estimation in the presence of unmeasured confounding, usuallyoccurring in non-randomized observational data common to fields such aseconomics and public health. However, such methods usually make constrictinglinearity and additivity assumptions that are inapplicable to the complexmodeling challenges of today. The growing body of observational data beingcollected will necessitate flexible regression modeling while also being ableto control for confounding using instrumental variables. Therefore, thisarticle presents a nonlinear instrumental variable regression model based onBayesian regression tree ensembles to estimate such relationships, includinginteractions, in the presence of confounding. One exciting application of thismethod is to use genetic variants as instruments, known as Mendelianrandomization. Body mass index is one factor that is hypothesized to have anonlinear relationship with cardiovascular risk factors such as blood pressurewhile interacting with age. Heterogeneity in patient characteristics such asage could be clinically interesting from a precision medicine perspective whereindividualized treatment is emphasized. We present our flexible Bayesianinstrumental variable regression tree method with an example from the UKBiobank where body mass index is related to blood pressure using geneticvariants as the instruments.

Flexible Instrumental Variable Models With Bayesian Additive Regression Trees