How to Reduce Data Variability in Respiratory Clinical Trials

Introduction [00:20]

Phil Lake and Dr. Kai Michael-Beeh examine the steps sponsors and study teams can take to improve data quality and reduce data variability in respiratory trials. They also discuss the innovations and trends they expect to see in the industry in the future.

How can sponsors and the pharma industry overcome unacceptable data variability from sites? [03:30]

Respiratory measurements can be complicated and challenging if study teams don’t stick to the fundamentals. This means that variability and bad data quality are common issues. Two major factors can contribute to variability: disease-associated factors and effort-dependent factors. Both of these issues can be managed, with strict standardization in the protocol and rigorous training, respectively.

Is a focus on ATS/ERS standards enough to generate research-grade data? [06:34]

ATS/ERS standards are a suitable starting point and principle for quality assurance. However, simply applying these rules is not sufficient. In addition to following these standards, sponsors should implement additional visual inspections and plausibility checks.

How can we use available technology and services to reduce data variability? [09:50]

Available technology and data should be used to guarantee the production of the highest quality data possible. Ensure that investigators are aware of what defines a good quality test in the context of a clinical trial, including how important it is to show changes in lung function. Increased overall communication with investigators decreases variability, as well as the number of patients needed (and ultimately costs.)

How can sponsors and sites best stay on track with projected timelines? [14:35]

Communicating with sites so they can more effectively allocate their resources, especially when a study has fallen behind, can be a crucial improvement. Checks should take place to ensure that third-party providers are ready to begin once the site is initiated.

What’s the real cost or impact to pharma when we produce variable data or run into study delays? [19:18]

Variable data and study delays have a tremendous impact by putting approvals at risk and jeopardizing the reputation of the drug or manufacturer. When studies produce better data, there’s an increased chance of achieving study goals with less patients and lower costs. Better data also improves confidence in negative results.

Any future innovations that will improve data quality? How can we do better as an industry to understanding drug effect? [21:38]

In the future, the ability to measure various aspects of airway function will improve due to the shift towards personalized medicine, with new methods regulated and approved by the FDA.