How to Reduce Data Variability in Respiratory Clinical Trials

<h3><strong>Introduction</strong> [00:20]</h3><p>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.</p><h3><strong>How can sponsors and the pharma industry overcome unacceptable data variability from sites? [03:30]</strong></h3><p>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.</p><h3><strong>Is a focus on ATS/ERS standards enough to generate research-grade data? [06:34]</strong></h3><p>ATS/ERS standards are a suitable starting point and ...