Maricela Cruz, PhD, is a biostatistician passionate about conducting public health research with a specific focus on improving the health of disenfranchised communities.
Dr. Cruz’s research centers on developing flexible and robust statistical methods to evaluate health care interventions, particularly using electronic health records, to inform clinical and public health practices. She specializes in analytic methods for pre-post longitudinal correlated data, including interrupted time series (ITS), time series analysis, longitudinal data, change-point detection, and difference-in-differences. Dr. Cruz additionally has experience in developing and evaluating prediction models. The pre-post methods she develops allow health systems to evaluate interventions over time, while her prediction model work helps identify at-risk encounters — both are critical in shaping effective health practices.
Dr. Cruz has developed and evaluated several ITS models to estimate the impact of new nursing structures (and similar health care interventions) on patient experience outcomes in the presence of potentially lagged (or anticipatory) treatment effects. To communicate and encourage access to her methods in the broader public health community, Dr. Cruz co-created the Robust Interrupted Time Series toolbox (https://biostatistics-kaust.github.io/robust_time_series_toolbox/), a stand-alone, user-friendly application implementing the Robust Multiple ITS model that Dr. Cruz developed in 2019 for single and multiple ITS.
Dr. Cruz provides statistical and scientific leadership in collaborative public health and health care delivery projects aimed at improving health outcomes for disenfranchised communities. She collaborates on several projects aiming to improve care for people with a history of suicide ideation and on projects designing and evaluating health care interventions, such as value-based drug formularies and clinical decision support systems for at-risk encounters.
Dr. Cruz obtained her PhD in statistics from the University of California Irvine in 2019. She is an affiliate assistant professor in biostatistics at the University of Washington.
Coley RY, Johnson E, Simon GE, Cruz M, Shortreed SM Racial/Ethnic Disparities in the Performance of Prediction Models for Death by Suicide After Mental Health Visits 2021 Jul;78(7):726-734. doi: 10.1001/jamapsychiatry.2021.0493. PubMed
Cruz M, Osilla KC, Paddock SM. Group cohesion and climate in cognitive behavioral therapy for first-time DUI offenders. Alcohol Treat Q. 2019 https://doi.org/10.1080/07347324.2019.1613941. PubMed
Cruz M, Gillen DL, Bender M, Ombao H. Assessing health care interventions via an interrupted time series model: Study power and design considerations. Stat Med. 2019 May 10;38(10):1734-1752. doi: 10.1002/sim.8067. Epub 2019 Jan 7. PubMed
Bender M, Murphy EA, Cruz M, Ombao H. System and unit-level care quality outcomes after reorganizing frontline care delivery to integrate clinical nurse leaders: a quasi-experimental time series study. J Nurs Admin. 2019 49(6), p. 315-322. PubMed
Cruz M, Bender M, Ombao H. A robust interrupted time series model for analyzing complex health care intervention data. Stat Med. 2017 Dec 20;36(29):4660-4676. doi: 10.1002/sim.7443. Epub 2017 Aug 29. PubMed
Safety planning and risk screening improved outcomes for adult patients.
Study uses geographic data to track change over time.
JSM TV, Aug. 6, 2024