Clinical trial professionals are no stranger to the fact that about 50 percent of new drug compounds fail to demonstrate efficacy in therapeutic trails. What they don’t always know is that most failures can be attributed to the lack of necessary efficacy data to support clinical trial claims.
The average trial adherence rates are only 43 to 78 percent, and the reality is that data from non-compliant patients can affect trial results to such an extent that they can make or break a candidate drug. Knowing this, it’s no wonder that trials are failing to demonstrate efficacy – but what can we do to change that and start controlling compliance?
The first thing is to use the right tools to monitor, analyze and improve data. Clinical researchers need strong, repeatable data, and medication adherence is the main determinant of the quality of that data. Research shows that medication adherence in clinical trials is both poor and highly variable among groups and individual patients, with only 34 percent of patients taking their medications as prescribed. It’s is critical for clinical trials that we move toward new adherence platforms that support robust data collection and encourage better adherence.
The next step to controlling compliance is to understand power and sample size. Power and sample size are critical components of trial data and justification, but even the best-designed trials can fail because of low adherence rates. Low adherence rates are a significant problem because incomplete data means decisions around dosing efficacy and safety might be made from faulty information – which may prevent or delay a trial from moving forward. Not to mention, as adherence rates drop, required sample size increases exponentially with the average FDA clinical trial costing $16,000 per subject.
Clinical trial professionals also need to manage dosing for best results. When participants don’t adhere to trial requirements, skip dosages or take medications off schedule, the efficacy data required to support recommendations around accurate dosing and interactions can be highly compromised. Systems like the CleverCap adherence platform can accurately record the multiple data points required to identify strong or poor adherence and critical dosing and timing data needed to understand interactions.
Lastly, it is important that we start moving toward data-driven adaptive trials. ‘Adaptive’ trials are designed to change course as they progress, relying on data to make changes to protocol instead of waiting for the next trial. Currently researchers have limited tools for making dosing decisions as they do not know if efficacy/safety issues are drug or adherence related. By having stronger (more powered) data collection on the more effective combinations, a trial can move into its next phase with a more targeted desired outcome and design.
Taking control of compliance will help clinical trial professionals get stronger data, forecast trial costs, improve statistical power and move forward faster. To learn more about medication adherence platforms such as CleverCap, the gold standard of medication adherences systems for clinical trial teams, check out How poor medication adherence is damaging your clinical trial data and how to fix it, fast.