Maricela Cruz (Department of Statistics, University of California, Irvine)

Title: Interrupted Time Series Models for Analyzing Complex Healthcare Interventions

Abstract: Now more than ever, patients, providers, resources and contexts of care interact in dynamic ways to produce various measurable health outcomes that often do not align with expectations. This complexity and interdependency make it difficult to assess the true impact of interventions designed to improve patient healthcare outcomes, in terms of both research design and statistical analysis. Healthcare intervention data can be modeled as interrupted time series (ITS): sequences of measurements for an outcome collected at multiple time points before and after an intervention. There are, however, limitations to the current statistical methodology for analyzing ITS data. Namely, contemporary methods restrict the interruption’s effect to a predetermined time point or remove data for which the effects of the intervention may not be realized. In addition, commonly used methods often neglect plausible differences in temporal dependence and volatility and restrict analyses to a single hospital unit.

In this talk, I will discuss novel statistical methods developed for evaluating the effect of interventions on health outcomes. I will present the ‘robust-ITS’ model, able to estimate (rather than merely assume) the lagged effect of an intervention on a health outcome. I will illustrate components of robust-ITS that allow researchers to determine whether health outcomes are more predictable (as measured by stronger temporal dependence and smaller variability), and thus more desirable, after an intervention. I will then introduce the ‘Robust Multiple ITS’ model, an extension to allow for the incorporation of multi-unit ITS data, as well as a supremum Wald test that allows one to formally test for the existence of a change point across unit specific mean functions. In total, this methodology accommodates crucial intricacies of interventions under real-world circumstances and overcomes many of the omissions and limitations of current approaches. I illustrate the methods by analyzing patient centered data from a hospital that implemented and evaluated a new care delivery model in multiple units.