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Editor’s Note: This article is part of a series examining popular peer-reviewed articles from years past called “Peer-Reviews Revisited: Why You Should Read Today.” You can read the other articles in this series here.
Patient enrollment for clinical trials is not a numbers game. A growing body of evidence suggests that aggressive enrollment goals and patient selection practices can introduce bias into patient selection and undermine trial efficacy conclusions.
“Either the investigator enrolls a patient who does not meet the inclusion/exclusion criteria and asks for a protocol exemption, or their view of subjective patient enrollment data is biased by the interest to push the enrollment rate up,” said Richard Walovitch, PhD, lead author of “Enrollment: More Than Numbers” in Applied Clinical Trials Online. “The first is easily tractable whereas the latter is more problematic, insidious, and requires active detailed monitoring.”
Dr. Walovitch, President of WorldCare Clinical, a contract research organization in Boston, raised three key points.
BICR reduces the potential for individual investigator bias in patient selection. In an open-label oncology trial, for example, an investigator may believe that the active treatment arm is more likely to benefit patients compared to the placebo arm. In an attempt to benefit a wider range of patients, he/she may choose to enroll more borderline eligible patients in the treatment arm. The intentions may be good, but the resulting selection bias may obscure the true effects of the drug.
“BICR can play an important role in strictly enforcing per-protocol criteria determining what patients get into the trial,” Dr. Walovitch said. “BCIR can positively impact trial outcomes to be more robust, homogeneous, and aligned to regulatory compliance with bias minimized.”