Onchee Yu, MS, is a biostatistician who has contributed her extensive experience in statistical applications to electronic health records (EHR) data to studies related to women’s health, pharmacoepidemiology, and vaccine safety and effectiveness. Ms. Yu has been a key member of Kaiser Permanente Washington Health Research Institute’s (KPWHRI) immunization research program for 20 years. Her work focuses on applying statistical methods to evaluate vaccine effectiveness, side effects, and safety. In collaboration with KPWHRI biostatisticians Jennifer Nelson, PhD, and Andrea Cook, PhD, Ms. Yu developed and improved statistical methods for monitoring the safety of postmarketing vaccines in the Vaccine Safety Datalink project.
Much of Ms. Yu's recent research is in pharmacoepidemiology, which is studying how drugs are used in a population and their impact on public health. She is an expert in statistical analysis in a complex, clinically important area—determining if taking medicine for one condition (for example, cardiovascular medications) affects risk of other illnesses (for example, cancer outcomes).
Ms. Yu also contributes to women’s health. Using extensive EHR data and in collaboration with University of Washington clinician and KPWHRI affiliate researcher Susan D. Reed, MD, MPH, Ms. Yu has estimated incidences and prevalences, validated diagnosis codes, and developed automated case-finding algorithms for women’s health conditions including uterine fibroids, adenomyosis and endometriosis.
Ms. Yu obtained her MS in biostatistics from the University of Washington in 1999. She is a member of the American Statistical Association and the International Society for Pharmacoepidemiology. Her statistical methodological expertise includes classification and regression tree analysis, and survival analysis.
Survival analysis; classification and regression tree analysis
Biostatistics; medication use and cancer outcomes
Biostatistics; vaccine safety and efficacy; postmarketing vaccine safety study design and analysis
Biostatistics; incidence and prevalence estimations; validation of diagnosis codes; automated case-finding algorithms
Biostatistics; medication use and cancer outcomes; postmarketing drug and vaccine safety study design and analysis; safety signal detection methods
Nelson JC, Shortreed SM, Yu O, Peterson D, Baxter R, Fireman B, Lewis N, McClure D, Weintraub E, Xu S, Jackson LA. Integrating database knowledge and epidemiological design to improve the implementation of data mining methods to evaluate vaccine safety in large healthcare databases. Stat Anal Data Min. 2014;7(5):33751.
Wirtz HS, Buist DS, Gralow JR, Barlow WE, Gray SL, Chubak J, Yu O, Bowles EJ, Fujii M, Boudreau DM. Frequent antibiotic use and second breast cancer events. Cancer Epidemiol Biomarkers Prev. 2013 Sep;22(9):1588-99. doi:10.1158/1055-9965.EPI-13-0454. Epub 2013 Jul 5. PubMed
Jackson ML, Yu O, Nelson JC, Naleway A, Belongia EA, Baxter R, Narwaney K, Jacobsen SJ, Shay DK, Jackson LA. Further evidence for bias in observational studies of influenza vaccine effectiveness: the 2009 Influenza A(H1N1) pandemic. Am J Epidemiol. 2013 Oct 15;178(8):1327-36. doi: 10.1093/aje/kwt124. Epub 2013 Aug 26. PubMed
Torrone EA, Satterwhite C, Scholes D, Yu O, Berman S, Peterman T. Estimating Chlamydia re-infection rates: an empirical example. Sex Transm Infect. 2013 Aug;89(5):388-90. doi: 10.1136/sextrans-2012-050970. Epub 2013 May 4. PubMed
Chubak J, Yu O, Buist DS, Wirtz HS, Boudreau DM. Time scale in follow-up studies: considering disease prognosis. Epidemiology. 2013;24(4):628-9. doi: 10.1097/EDE.0b013e3182961708. PubMed
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