Presentation: Interrupted Time Series Models for Assessing Complex Health Care Interventions
Speaker: Maricela Cruz, PhD, Assistant Investigator, Biostatistics Unit, Kaiser Permanente Washington Health Research Institute
Abstract: 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 data a priori restrict the interruption’s effect to a predetermined time point or censor data for which the intervention effects may not be fully realized, and neglect changes in the temporal dependence and variability. In addition, current methods limit the analysis to one hospital unit or entity and are not well specified for discrete outcomes (e.g., patient falls). In this talk, I present novel ITS methods based on segmented regression that address the aforementioned limitations.
I briefly introduce the ‘Robust-ITS’ model, a model able to estimate (rather than merely assume) the lagged effect of an intervention on a health outcome, and the ‘Robust Multiple ITS’ model, an extension to allow for the incorporation of multi-unit ITS data. Then, I present 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, count and rate responses. The proposed methods borrow information across units in multi-unit settings and provide a testing paradigm for the existence of a change point along with an estimate of the change point conditional upon the determination of its existence. Tests for differences in the mean function and correlation structure pre- and post-intervention are also given. The methodology is illustrated by analyzing patient centered data from a hospital that implemented and evaluated a new care delivery model in multiple units.