Statistical Considerations for Analyzing and Interpreting Data from Biometric Monitoring Technologies (BioMeTs) in Clinical Trials (Preprint)

Abstract

UNSTRUCTURED The proliferation and increasing maturity of biometric monitoring technologies allows clinical investigators to measure the health status of trial participants in a more holistic manner, especially outside of clinical settings. This includes capturing meaningful aspects of health in daily living and in a more granular and objective manner compared to traditional tools in clinical settings. Within multidisciplinary teams, statisticians and data scientists are increasingly involved in clinical trials that incorporate digital clinical measures and are called upon to provide input into study planning, generation of evidence on the clinical validity of novel clinical measures, and evaluation of the adequacy of existing evidence. Analysis objectives related to demonstrating clinical validity of novel clinical measures differ from typical objectives related to demonstrating safety and efficacy of therapeutic interventions using established measures which statisticians are most familiar with. This paper discusses statistical considerations for generating evidence for clinical validity through the lens of the type and intended use of a clinical measure. This paper also briefly discusses the regulatory pathways through which clinical validity evidence may be reviewed and highlights challenges that statisticians and data scientists may encounter while dealing with data from biometric monitoring technologies.