Cary Gross: We need to learn to analyze real-world evidence rigorously

Share on facebook
Share on twitter
Share on linkedin
Share on email
Share on print

Cary Gross, professor of medicine and of epidemiology at Yale School of Medicine, has been working with a dataset of 35,000 non-small cell lung cancer patients, looking for signs of disparities in access to PD-1 checkpoint inhibitors.

To access this subscriber-only content please log in or subscribe.

If your institution has a site license, log in with IP-login or register for a sponsored account.*
*Not all site licenses are enrolled in sponsored accounts.

Login Subscribe
Matthew Bin Han Ong
Matthew Bin Han Ong

YOU MAY BE INTERESTED IN

When our hematological malignancy testing pilot project began in Eldoret, Kenya, there seemed to be a mismatch in relation to progress in healthcare. The region, like much of sub-Saharan Africa, had been focusing on combatting infectious diseases such as HIV and malaria—which was much-needed—yet cancer care was under-resourced. 
Artificial intelligence is rapidly transforming biomedical research and healthcare. Large language models, foundation models, and AI agents are increasingly being deployed to assist with data interpretation, literature review, clinical decision support, and translational research. 
In modern oncology, important insights from clinical trials often emerge years after initial publication. As new therapies extend survival and transition more patients into long-term remissions, clinicians and researchers are increasingly looking beyond initial response rates to understand durability, long-term safety, and even the possibility of a cure. 
Matthew Bin Han Ong
Matthew Bin Han Ong

Never miss an issue!

Get alerts for our award-winning coverage in your inbox.

Login