A Generalized Interrupted Time Series Model for Assessing Complex Health Care Interventions

Maricela Cruz (), Hernando Ombao and Daniel L. Gillen
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Maricela Cruz: Kaiser Permanente Washington Health Research Institute
Hernando Ombao: King Abdullah University of Science and Technology
Daniel L. Gillen: University of California Irvine

Statistics in Biosciences, 2022, vol. 14, issue 3, No 11, 582-610

Abstract: 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 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.

Keywords: Change point detection; Complex interventions; Discrete outcomes; Interrupted time series; Patient-centered data; Segmented regression (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s12561-022-09346-6

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