Borrowing strength between unaligned binary time-series via Bayesian nonparametric rescaling of Unified Skewed Normal priors

Abstract: We define a Bayesian semi-parametric model to effectively conduct inference with unaligned longitudinal binary data. The proposed strategy is motivated by data from the Human Epilepsy Project (HEP), which collects seizure occurrence data for epilepsy patients, together with relevant covariates. The model is designed to flexibly accommodate the particular challenges that arise with such data. First, epilepsy data require models that can allow for extensive heterogeneity, across both patients and time. With this regard, state space models offer a flexible, yet still analytically amenable class of models. Nevertheless, seizure time-series might share similar behavioral patterns, such as local prolonged periods of elevated seizure presence, which we refer to as "clumping". Such similarities can be used to share strength across patients and define subgroups. However, due to the lack of alignment, straightforward hierarchical modeling of latent state space parameters is not practicable. To overcome this constraint, we construct a strategy that preserves the flexibility of individual trajectories while also exploiting similarities across individuals to borrow information through a nonparametric prior. On the one hand, heterogeneity is ensured by (almost) subject-specific state-space submodels. On the other, borrowing of information is obtained by introducing a Pitman-Yor prior on group-specific probabilities for patterns of clinical interest. We design a posterior sampling strategy that leverages recent developments of binary state space models using the Unified Skewed Normal family (SUN). The model, which allows the sharing of information across individuals with similar disease traits over time, can more generally be adapted to any setting characterized by unaligned binary longitudinal data.