Back to search

Article

Using Machine-Learning Techniques to Identify Responders vs. Non-responders in Randomized Clinical Trials.

2020-11-23

Abstract excerpt

Despite the expectation of heterogeneity in therapy outcomes, especially for complex diseases like cancer, analyzing differential response to experimental therapies in a randomized clinical trial (RCT) setting is typically done by dividing patients into responders and non-responders, usually based on a single endpoint. Given the existence of biological and patho-physiological differences among metastatic colorecta...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
e0ca5135-89e2-5227-9270-e81c70340d3e
DOI
10.1101/2020.11.21.20232041
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Using Machine-Learning Techniques to Identify Responders vs. Non-responders in Randomized Clinical Trials.DOI 10.1101/2020.11.21.20232041
Select a neighboring publication to make it the new centre.