A Generalized Interrupted Time Series Model for Assessing Complex Health Care Interventions (306306)*Maricela F. Cruz, University of California, Irvine
Daniel L Gillen, University of California, Irvine
Hernando Ombao, King Abdullah University of Science and Technology
Keywords: Interrupted time series, segmented regression, complex interventions, patient-centered data, change point detection, discrete outcomes
Assessing the impact of complex interventions on measurable health outcomes is a growing concern in healthcare and health policy. Interrupted time series (ITS) designs borrow from traditional case-crossover designs and function as quasi-experimental methodology able to retrospectively analyze the impact of an intervention. Statistical models used to analyze ITS designs primarily focus on continuous valued outcomes. We propose the “Generalized Robust ITS” (GRITS) model appropriate for outcomes whose underlying distribution belongs to the family of exponential distributions, thereby expanding the available methodology to adequately model binary and count responses. Alongside GRITS we present a test for the existence of a change point for discrete outcomes. The methods proposed are able to test for the existence of and estimate the change point, borrow information across units in multi-unit settings, and test for differences in the mean function and correlation pre- and post-intervention. The methodology is illustrated by analyzing patient centered data from a hospital that implemented and evaluated a new care delivery model in multiple units.