How External Control Arms Work | MIT Technology Review & Medidata Solutions

Written in partnership with MIT Technology Review 

To build an external control arm, researchers mine a database of past clinical trials to find control subjects who are close matches to the patients in the treatment arm. The information available on the subjects in the database lets researchers match subjects using relevant criteria, such as age, gender, weight, type of condition, and marital status.

Medidata uses artificial intelligence to plumb its database and find patients who served as controls in past trials of treatments for a certain condition to create its proprietary version of external control arms. “We can carefully select these historical patients and match the current-day experimental arm with the historical trial data,” says Arnaub Chatterjee, senior vice president for products, Acorn AI at Medidata. The trials and the patients are matched for the endpoints of the study and for other aspects of the study designs, such as the type of data collected at the beginning of the study and along the way.

When creating an external control arm, “we do everything we can to mimic an ideal randomized controlled trial,” says Ruthie Davi, vice president of data science, Acorn AI at Medidata. The first step is to search the database for possible control arm candidates using the key eligibility criteria from the investigational trial: For example, the type of cancer, the key features of the disease and how advanced it is, and whether it’s the patient’s first time being treated. It’s essentially the same process used to select control patients in a standard clinical trial—except data recorded at the beginning of the past trial, rather than the current one, is used to determine eligibility, Davi says. “We are finding historical patients who would qualify for the trial if they existed today.” Once this basic screening stage is completed, statistical matching and weighting techniques are used to match the possible control patients with the patients in the test arm.

External control arms can make clinical trials less expensive by reducing the number of patients that need active management. Nicholas Borys, chief medical officer for biotechnology company Celsion, estimates that each cancer patient costs the company tens of thousands of dollars to enroll in a trial and follow throughout the entire protocol. Plus, using external controls may make a study more appealing to potential participants, expediting recruitment. “Patients might be more interested because they know everyone will be getting the treatment,” Borys says.

Celsion has tried an external control arm to assess a new compound intended for patients with late-stage ovarian cancer. The drug seemed to work well in a test group, but at that point the study did not have a control arm. Acorn AI at Medidata compiled an external control arm that was a near-perfect match for the characteristics of the test group, and a comparison of the two groups showed good enough results to justify Phase 2 trials. “It certainly convinced us that our drug had an effect,” Borys says.

For mid-sized and emerging biopharma companies, Medidata’s 20+ years of expertise, dedicated support, and continuous learning with customers and trials of all sizes help you stay on the path to success.

Download our white paper with MIT Technology Review, Clinical Trials are Better, Faster, Cheaper with Big Data, for a comprehensive guide to external control arms in clinical trials.