Title: Dynamic modeling of conditional quantile trajectories, with application to longitudinal snippets
Authors: Hans-Georg Mueller - University of California Davis (United States) [presenting]
Abstract: Longitudinal data are often plagued with sparsity of time points where measurements are available. The functional data analysis perspective has been shown to provide an effective and flexible approach to address this problem for the case where measurements are sparse but their times are randomly distributed over an interval. We focus here on a different scenario where available data can be characterized as snippets, which are very short stretches of longitudinal measurements. For each subject the stretch of available data is much shorter than the time frame of interest, a common occurrence in accelerated longitudinal studies. An added challenge is introduced if a time proxy that is basic for usual longitudinal modeling is not available, as encountered in situations where time of disease onset is unknown and chronological age does not provide a meaningful time reference for longitudinal modeling. To address these challenges, we introduce conditional quantile trajectories for monotonic processes as solutions of a dynamic system. Conditional quantile trajectories emerge as useful descriptors of processes that quantify deterioration over time.