Department of Medicine Statistics Core
Department of Medicine Statistics Core
(DOMStat)
About Us
The Department of Medicine Statistics Core (DOMStat) comprises a team of 35 seasoned statisticians proficient in providing top-tier statistical support to researchers within the Department of Medicine. Our core faculty engages in collaborative efforts with investigators across various phases of research endeavors, including grant preparation, data analysis, and manuscript composition.
We have a proven track record of successful partnerships spanning an extensive array of research domains, encompassing anesthesiology, behavioral and social sciences, bioinformatics, cardiology, clinical trials, dentistry, endocrinology, health economics, geriatrics, gastroenterology, health services research, hepatology, infectious disease, neurology, nephrology, nursing, nutrition, obstetrics/gynecology, oncology, pediatrics, psychology, public health, pulmonology, quality improvement, rheumatology, sleep disorders, sports medicine, substance use, surgery, organ transplantation, and urology.
Beyond proficiency in current statistical methodologies, our statisticians actively engage in the development of innovative statistical approaches tailored to specific research needs. Moreover, we provide comprehensive support for study database construction and data management, particularly for ongoing clinical trials and observational studies.
DOMStat possesses extensive experience in handling diverse datasets, including high throughput omics data, patient-level data from clinical trials, bio-specimen data, electronic health records data, survey data, and large-scale secondary health services/insurance claims data. This broad expertise ensures robust and meticulous statistical analyses across a wide spectrum of research projects.
Our Director
ProfileExplore cutting-edge statistical services tailored for Administrative and Electronic Health Research Data at our dedicated research unit. Our team provides expert support in data analysis, database construction, and methodological innovation. Partner with us to enhance your research outcomes with meticulous statistical insights and comprehensive data management solutions.
Engage with our dedicated team specializing in Basic Sciences and Bioinformatics Services. We offer expert statistical support tailored for biological research, including genomic analysis, data integration, and computational modeling. Partner with us to leverage advanced methods and bioinformatics tools for groundbreaking insights and innovations in basic sciences.
Discover our dedicated Clinical Trials and Data Management Services unit, providing comprehensive support from protocol development through to data analysis and regulatory compliance. Our team specializes in optimizing research efficiency and reliability, offering robust statistical insights that drive impactful clinical discoveries and advancements in healthcare.
Experience our specialized team offering cutting-edge Machine Learning and Artificial Intelligence Services. We excel in developing innovative algorithms, leveraging advanced methods to extract insights from complex data. Partner with us to harness AI for transformative research and actionable outcomes across diverse fields.
Department Highlights
Faculty Highlight
Dr. Alexandra M. Klomhaus is a biostatistician specializing in methods for analyzing complex longitudinal electronic health record (EHR) data. Her work addresses key challenges in observational medical research, including irregular visit timing, sparse outcomes, and treatment-dependent data. She earned her Ph.D. in Biostatistics from UCLA in 2021 and her B.S. in Mathematics & Applied Science from UCLA in 2012. Her research advances rigorous, data-driven approaches to improve insights from real-world clinical data.
Project Highlight
The GRAND Study is a multicenter randomized clinical trial focused on preventing type 2 diabetes in women with a history of gestational diabetes, prediabetes, and elevated BMI. Leveraging electronic health record data and patient-reported outcomes, the study evaluates how shared decision-making interventions influence health behaviors and risk reduction. Findings show that perceived diabetes risk alone does not drive meaningful behavior change, highlighting the need for more tailored and supportive prevention strategies . This work is advancing more effective, patient-centered approaches to diabetes prevention in high-risk populations.
