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

Weighting is a general and often-used method for statistical adjustment.Weighting has two objectives: first, to balance covariate distributions, andsecond, to ensure that the weights have minimal dispersion and thus produce amore stable estimator. A recent, increasingly common approach directlyoptimizes the weights toward these two objectives. However, this approach hasnot yet been feasible in large-scale datasets when investigators wish toflexibly balance general basis functions in an extended feature space. Forexample, many balancing approaches cannot scale to national-level healthservices research studies. To address this practical problem, we describe ascalable and flexible approach to weighting that integrates a basis expansionin a reproducing kernel Hilbert space with state-of-the-art convex optimizationtechniques. Specifically, we use the rank-restricted Nystr\"{o}m method toefficiently compute a kernel basis for balancing in {nearly} linear time andspace, and then use the specialized first-order alternating direction method ofmultipliers to rapidly find the optimal weights. In an extensive simulationstudy, we provide new insights into the performance of weighting estimators inlarge datasets, showing that the proposed approach substantially outperformsothers in terms of accuracy and speed. Finally, we use this weighting approachto conduct a national study of the relationship between hospital profit statusand heart attack outcomes in a comprehensive dataset of 1.27 million patients.We find that for-profit hospitals use interventional cardiology to treat heartattacks at similar rates as other hospitals, but have higher mortality andreadmission rates.

Scalable kernel balancing weights in a nationwide observational study of hospital profit status and heart attack outcomes