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.
Li D, Cruz M, Mooney SJ, Cook AJ, Bobb JF Mitigating the risk of bias exacerbation when controlling for unmeasured spatial confounding for binary exposures 2026 Mar 17;195(4):1063-1072 doi:10.1093/aje/kwaf248. doi: 10.1093/aje/kwaf248. Epub 2025-11-07. PubMed
Ye S, Cruz M, Wang Z, Yu Y A marginalized three-part interrupted time series regression model for proportional data 2026 Jan 18; doi.org/10.1080/02664763.2026.261475. doi: 10.1080/02664763.2026.2614752. Epub 2026-01-18. PubMed
Cruz M, Shortreed SM, Simon GE, Coley Y Evaluating Clinical Implementation of Risk Prediction Based Interventions Using Difference-In-Differences 2025 Jul 21 doi: 10.1111/1475-6773.70015. Epub 2025-07-21. PubMed
Angerhofer JE, Cruz M, Shaw J, Stewart C, Runkle A, Wolter E, Holden E, Medlock S, Quintana L, Kuo EN, Trejo J, King R, Boggs J. Health care use preceding suicide by firearm compared with suicide by other means -- Alaska, Colorado, and Washington, 2020-2022. MMWR Morb Mortal Wkly Rep. 2025 Jun 12;74(21):365-371. doi: 10.15585/mmwr.mm7421a2. PubMed
Angerhofer JE, Cruz M, Shaw J, Stewart C, Runkle A, Wolter E, Holden E, Medlock S, Quintana L, Kuo EN, Trejo J, King R, Boggs J Health Care Use Preceding Suicide by Firearm Compared with Suicide by Other Means -- Alaska, Colorado, and Washington, 2020-2022. 2025 Jun 12;74(21):365-371 doi:10.15585/mmwr.mm7421a2. doi: 10.15585/mmwr.mm7421a2. 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