Digital health technologies (DHTs) enable us to measure human physiology and behavior remotely, objectively, and continuously. With the accelerated adoption of DHTs in clinical trials, there is an unmet need to identify statistical approaches to address missing data to ensure that the derived endpoints are valid, accurate, and reliable.

In this article, we discuss characteristics of missing data collected by DHT and review emerging statistical approaches for addressing missingness in epoch-level data. We discuss strategies for minimizing missing data by optimizing DHT deployment and by including the patients’ perspectives in the study design.