Assessing the impact of complex interventions on measurable health outcomes is a growing concern in health care 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 exponential family of distributions, thereby expanding the available methodology to adequately model binary and count responses. GRITS formally implements a test for the existence of a change point in discrete ITS. The methodology proposed is 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 falls from a hospital that implemented and evaluated a new care delivery model in multiple units.
Bibliographical noteKAUST Repository Item: Exported on 2022-06-06
Acknowledgements: University of California Irvine Eugene Cota-Robles Fellowship; National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1321846; National Science Foundation MMS 1461534 and DMS 1509023 grants; National Institute on Aging of the National Institutes of Health R01AG053555 and P50AG16573; National Institute of Mental Health of the National Institutes of Health MH115697.