Events

With recent developments in wearable biosensors and portable electronics (collectively referred to as digital health technology – DHT), many clinical trials propose measuring endpoints using a DHT.  DHTs record data at a higher frequency than traditional clinician/patient observed data.  For example, a wearable accelerometer records data multiple times a second every day during follow-up (lasting multiple weeks).  Clinical endpoints are derived from this high frequency data.  Examples include:

• Trials where an experimental drug is hypothesized to improve exercise capacity with the endpoint of daily time spent at or above a specified exercise intensity
• Trials where daily sleep parameters are measured by a one or more sensors

To estimate and test treatment effects measured in these cases, several statistics issues must be addressed.  Unlike current endpoint measurements, DHTs provide endpoint measurements for every day worn.  In the case that different days (e.g., weekends vs. weekdays) are combined to form endpoints, treatment effect estimates may be biased or miss important disease features if days are not exchangeable.  With the greater amount of data collected, missing data can occur in multiple ways (e.g., a missing observation within a day, missing a day within a week, monotone dropout).

This talk discusses potential issues arising in exercise endpoints if a DHT is worn for different times each day during a study.  It also discusses a case study exploring how different statistical analyses of a clinical trial for a new insomnia drug are affected by missing data to motivate discussion of statistical considerations in studies using these new data collection tools.

Bio:

Dr. Andrew Potter is a mathematical statistician in the Division of Biometrics I in CDER supporting the review work in the Division of Psychiatry.  His research interests include the use of digital health technologies in clinical trials and the analysis of high frequency outcome data and in involved in working groups at FDA on this topic.  He received his PhD in Biostatistics from the University of Pittsburgh.

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