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.
Yeung K, Cruz M, Tsiao E, Watkins JB, Sullivan SD Drug use and spending under a formulary informed by cost-effectiveness 2023 Nov;29(11):1175-1183. doi: 10.18553/jmcp.2023.29.11.1175. PubMed
Shortreed SM, Walker RL, Johnson E, Wellman R, Cruz M, Ziebell R, Coley RY, Yaseen ZS, Dharmarajan S, Penfold RB, Ahmedani BK, Rossom RC, Beck A, Boggs JM, Simon GE Complex modeling with detailed temporal predictors does not improve health records-based suicide risk prediction 2023 Mar 23;6(1):47. doi: 10.1038/s41746-023-00772-4. Epub 2023-03-23. PubMed
Cruz M, Ombao H, Gillen DL A generalized interrupted time series model for assessing complex health care interventions 2022 Dec;14(3):582-610. doi: 10.1007/s12561-022-09346-6. Epub 2022-05-25. PubMed
Cruz M, Drewnowski A, Bobb JF, Hurvitz PM, Vernez Moudon A, Cook A, Mooney SJ, Buszkiewicz JH, Lozano P, Rosenberg DE, Kapos F, Theis MK, Anau J, Arterburn D Differences in weight gain following residential relocation in the Moving to Health (M2H) Study 2022 Sep;33(5):747-755. doi: 10.1097/EDE.0000000000001505. Epub 2022-05-20. PubMed
Cruz M, Shortreed SM, Richards JE, Coley RY, Yarborough BJ, Walker RL, Johnson E, Ahmedani BK, Rossom R, Coleman KJ, Boggs JM, Beck AL, Simon GE Machine Learning Prediction of Suicide Risk Does Not Identify Patients Without Traditional Risk Factors 2022 Aug 31;83(5). doi: 10.4088/JCP.21m14178. Epub 2022-08-31. 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