  Intensive longitudinal biomarker data are increasingly common in scientific
studies that seek temporally granular understanding of the role of behavioral
and physiological factors in relation to outcomes of interest. Intensive
longitudinal biomarker data, such as those obtained from wearable devices, are
often obtained at a high frequency typically resulting in several hundred to
thousand observations per individual measured over minutes, hours, or days.
Often in longitudinal studies, the primary focus is on relating the means of
biomarker trajectories to an outcome, and the variances are treated as nuisance
parameters, although they may also be informative for the outcomes. In this
paper, we propose a Bayesian hierarchical model to jointly model a
cross-sectional outcome and the intensive longitudinal biomarkers. To model the
variability of biomarkers and deal with the high intensity of data, we develop
subject-level cubic B-splines and allow the sharing of information across
individuals for both the residual variability and the random effects
variability. Then different levels of variability are extracted and
incorporated into an outcome submodel for inferential and predictive purposes.
We demonstrate the utility of the proposed model via an application involving
bio-monitoring of hertz-level heart rate information from a study on social
stress.
