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

Individualized treatment rules (ITRs) for treatment recommendation is animportant topic for precision medicine as not all beneficial treatments workwell for all individuals. Interpretability is a desirable property of ITRs, asit helps practitioners make sense of treatment decisions, yet there is a needfor ITRs to be flexible to effectively model complex biomedical data fortreatment decision making. Many ITR approaches either focus on linear ITRs,which may perform poorly when true optimal ITRs are nonlinear, or black-boxnonlinear ITRs, which may be hard to interpret and can be overly complex. Thisdilemma indicates a tension between interpretability and accuracy of treatmentdecisions. Here we propose an additive model-based nonlinear ITR learningmethod that balances interpretability and flexibility of the ITR. Our approachaims to strike this balance by allowing both linear and nonlinear terms of thecovariates in the final ITR. Our approach is parsimonious in that the nonlinearterm is included in the final ITR only when it substantially improves the ITRperformance. To prevent overfitting, we combine cross-fitting and a specializedinformation criterion for model selection. Through extensive simulations, weshow that our methods are data-adaptive to the degree of nonlinearity and canfavorably balance ITR interpretability and flexibility. We further demonstratethe robust performance of our methods with an application to a cancer drugsensitive study.

A reluctant additive model framework for interpretable nonlinear individualized treatment rules