Title: Interrupted Time Series Models for Assessing Complex Health Care Interventions
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. According to the 2018 Annual Review of Public Health, interrupted time series (ITS) designs may be the only feasible recourse for studying the impacts of large-scale public health policies. 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 and provide a testing paradigm for the existence of a change point in the time series. The methodology is illustrated by analyzing patient centered data from a hospital that implemented and evaluated a new care delivery model in multiple units.
Maricela Cruz is an Assistant Investigator and Biostatistician at Kaiser Permanente Washington Health Research Institute and Affiliate Assistant Professor at the University of Washington Department of Biostatistics. She received her PhD in statistics from the University of California Irvine and was a National Science Foundation Graduate Research Fellowship awardee and Eugene Cota-Robles fellow during her time there. Maricela’s research primarily focuses on developing novel statistical methods to assess and evaluate the impact of complex health interventions.
