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

Purpose Although randomized clinical trials are considered the gold standard in biomedicine, they have several limitations such as experimenting on a sample group under ideal set forth criteria, creating an environment that does not apply to daily clinical practice where comorbid conditions are encountered everyday, and ignoring the differential response to treatment and treatment-related risk detected in the study population. This is due in part to the inherent complexity of the disease process and the heterogeneity within and between populations – two fundamental principles of biology that currently do not receive sufficient attention in study design. The advent of high throughput technologies of genomics, proteomics, and deep sequencing has demonstrated the heterogeneous nature of biological data. In this paper, we propose to demonstrate how high throughput data combined with evolutionary-based analyses could be used to stratify patients prior to starting the clinical study into groups that share similar traits and affinities. With this information in hand, participants can then be randomized to control or treatment groups within each phyletic clade (or population).

P02.59. Complexity of disease and population heterogeneity require a rethinking of clinical trial design