Implementing Different Imputation Pipelines for High-Frequency Digital Biomarkers with Missing Data and Lower-Frequency Binary Outcomes

• Activity Number: 352 - SPEED: Biometrics and Environmental Statistics Part 2
• Type: Contributed
• Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 2:45 PM
• Sponsor: Biometrics Section
• Author(s): Nicole Wakim* and Hiroko Dodge and Zhenke Wu and Tom Braun
• Companies: University of Michigan and Oregon Health and Science University and University of Michigan, Ann Arbor and University of Michigan
• Keywords: digital biomarkers; time granularity; data processing; measurement frequency; longitudinal analysis

Abstract

High-frequency digital biomarkers can improve the detection and prediction of human disorders and diseases. However, it is challenging to leverage biomarkers in traditional modeling techniques as the outcome of interest is often assessed less frequently than the biomarkers. In addition, biomarkers rarely are completely observed. Thus, it is necessary to summarize biomarker data to match the outcome’s frequency while simultaneously imputing missing biomarker values. Via simulation, we assess two processes involving imputation of biomarkers for longitudinal analysis of a binary outcome: (1) Impute then Summarize (IS), where we impute at the finest time granularity (daily), then summarize, and (2) Summarize then Impute (SI), where we summarize the biomarker then impute at the summary level. Our results show that accuracy of coefficient estimation depends on percent missing data, length of consecutive missing days, and the rate of trajectory change of the biomarkers. A real data analysis involving mild cognitive impairment is used to demonstrate the empirical differences.