Maricela Cruz, PhD

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“Developing flexible yet pragmatic and robust statistical methodology that can accommodate the intricacies of real-world circumstances is paramount in addressing public health concerns.”

Maricela Cruz, PhD

Assistant Biostatistics Investigator, Kaiser Permanente Washington Health Research Institute

Biography

Maricela Cruz, PhD, strives to create useful and widely applicable methodology to tackle real-world problems in public health. Her research primarily focuses on developing novel statistical methods to assess and evaluate the impact of complex health care interventions. In her research, she uses statistical techniques for interrupted time series, time series, correlated data, change point detection, longitudinal data and interventions research.

Dr. Cruz received her PhD in statistics from the University of California Irvine. During her time there, she was a National Science Foundation Graduate Research Fellowship awardee and Eugene Cota-Robles fellow. She worked with care delivery experts and practitioners to design and conduct statistical analyses that assessed health care interventions across multiple hospitals and hospital units. She developed two novel interrupted time series models that allow for complex correlation structures that can change post intervention and are adequate for single- and multi-unit continuous data. To communicate and encourage access to her methods by non-statisticians in the broader public health community, Dr. Cruz produced an application in R Shiny that analyzes interrupted time series data. As a graduate student, she was a summer associate at the RAND Corporation. While at RAND, she led a collaborative study examining the relationship between group cohesion and climate with alcohol use outcomes in a group therapy intervention for individuals with a first-time offense for driving under the influence.

At KPWHRI, Dr. Cruz works alongside researchers in behavioral health, mental health, and insurance design. She explores the relationship between weight change and diabetes measures and the built environment, aids in the development of suicide risk prediction algorithms, and evaluates interventions that encourage high-value use of health care services.

Recent Publications

Simon GE, Cruz M, Boggs JM, Beck A, Shortreed SM, Coley RY. Predicting outcomes of antidepressant treatment in community practice settings.  Psychiatr Serv. 2023 Dec 5:appips20230380. doi: 10.1176/appi.ps.20230380. [Epub ahead of print]. PubMed

Yeung L, Cruz M, Tsiao E, Watkins JB, Sullivan SD. Drug use and spending under a formulary informed by cost effectiveness. J Manag Care Spec Pharm. 2023 Nov;29(11):1175-1183. doi: 10.18553/jmcp.2023.29.11.1175. PubMed

Simon GE, Cruz M, Shortreed SM, Sterling SA, Coleman KJ, Ahmedani BK, Yaseen ZS, Mosholder AD. Stability of suicide risk prediction models during changes in health care delivery.  Psychiatr Serv. 2023 Aug 17:0. doi: 10.1176/appi.ps.20230172. [Epub ahead of print]. 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.  NPJ Digit Med. 2023;6(1):47. doi: 10.1038/s41746-023-00772-4.  PubMed

Cruz M, Ombao H, Gillen DL. A generalized interrupted time series model for assessing complex health care interventions.  Stat Biosci. 2022;14(3):582-610. doi: 10.1007/s12561-022-09346-6. Epub 2022 May 25.  PubMed

 

New findings

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Simpler models to identify suicide risk perform similarly to more complex ones

Models that are easier to explain, use could have better uptake in health care settings.

M2H study

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Can where you move impact future weight gain?

A new study finds that moving from low- to high-density neighborhoods might be related to reductions in weight gain.

Research

urban setting apartments and skyscrapers obesity and the built environment

Built environment plays little role in weight gain

New research suggests fast food and other aspects of built environments don’t affect weight, contrary to earlier findings.