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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We define personalized medicine as the administration of treatment to only persons thought most likely to benefit, typically those at high risk for mortality or another detrimental outcome. To evaluate personalized medicine, we propose a new design for a randomized trial that makes efficient use of high-throughput data (such as gene expression microarrays) and clinical data (such as tumor stage) collected at baseline from all participants. Under this design for a randomized trial involving experimental and control arms with a survival outcome, investigators first estimate the risk of mortality in the control arm based on the high-throughput and clinical data. Then investigators use data from both randomization arms to estimate both the effect of treatment among all participants and among participants in the highest prespecified category of risk. This design requires only an 18.1% increase in sample size compared with a standard randomized trial. A trial based on this design that has a 90% power to detect a realistic increase in survival from 70% to 80% among all participants, would also have a 90% power to detect an increase in survival from 50% to 73% in the highest quintile of risk.

Designing a Randomized Clinical Trial to Evaluate Personalized Medicine: A New Approach Based on Risk Prediction