A doubly hierarchical change point model for multi-unit interrupted time series in healthcare

Music City Center 

Interrupted time series (ITS) designs are aptly situated for studying the impacts of large-scale public health policies, as they borrow from case-crossover designs and can retrospectively assess the impact of an intervention. There have been many recent advances in the ITS methods literature, including, a formal test of the existence of a change point, change point estimation procedures in settings warranting it, models allowing for post-intervention changes in higher order moments, and models estimating marginal effects. To the best of our knowledge, no ITS methods with change point estimation procedures quantify the uncertainty of the estimated change point. We propose a Bayesian doubly hierarchical change point model that will detect unit specific change points and quantify their uncertainty while borrowing information across units. The model will incorporate multiple units, estimate a global over all units change point (and its variance), and account for changes in temporal dependence post-intervention. We demonstrate the methodology by analyzing multi-unit patient centered data from a hospital that implemented a new care delivery model.

Section on Bayesian Statistical Science