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

One primary goal of precision medicine is to estimate the individualizedtreatment rules (ITRs) that optimize patients' health outcomes based onindividual characteristics. Health studies with multiple treatments arecommonly seen in practice. However, most existing ITR estimation methods weredeveloped for the studies with binary treatments. Many require that theoutcomes are fully observed. In this paper, we propose a matching-based machinelearning method to estimate the optimal ITRs in observational studies withmultiple treatments when the outcomes are fully observed or right-censored. Weestablish theoretical property for the proposed method. It is compared with theexisting inverse probability weighting-based ITR estimation methods insimulation studies and a hepatocellular carcinoma study.

Multicategory Matched Learning for Estimating Optimal Individualized  Treatment Rules in Observational Studies with Application to a Hepatocellular  Carcinoma Study