Pamela Shaw, PhD, MS, is a biostatistician with expertise in clinical trials, design and analysis of complex epidemiologic studies, measurement error, and survival analysis. Dr. Shaw’s current statistical research includes a focus on methodology to correct for covariate and outcome measurement error, with application to studies reliant on electronic health records and large observational cohort studies. She is currently applying these methods to study the relationship between maternal weight trajectories during pregnancy and early childhood outcomes, as well as to identify risk factors for poor outcomes in several cohorts of patients with HIV/AIDS.
Dr. Shaw is also involved in studies of aging, behavioral intervention studies, and the use of biomarkers to calibrate self-reported nutritional intake and physical activity. She is co-investigator in a clinical trial that will assess whether an anti-inflammatory diet can improve cognition in a middle-aged (40- to 65-year-old) multi-ethnic urban population relative to a usual diet. She is an investigator for the Adult Changes in Thought (ACT) study, a joint project between Kaiser Permanente Washington Health Research Institute and the University of Washington that focuses on risk factors for dementia, including Alzheimer's disease, and declines in memory and thinking. For this study, she is collaborating with other ACT investigators to understand the best way to quantify patterns of physical activity in the 24-hour day and its association with health outcomes.
Before joining KPWHRI as a senior investigator, Dr. Shaw was an associate professor of biostatistics in the University of Pennsylvania Perelman School of Medicine. At UPenn she taught in the biostatistics graduate program and was the lead statistician for several early phase clinical trials, including studies of CART19, a novel CAR T cell immune therapy for the treatment of acute lymphocytic leukemia and other blood cancers, as well as clinical trials that evaluated the efficacy of behavioral economic interventions to increase healthy behaviors. She co-authored the textbook Essentials of Probability Theory for Statisticians (CRC Press 2016).
Prior to UPenn, she was a mathematical statistician in the Biostatistics Research Branch at the National Institute of Allergy and Infectious Diseases, where she was lead statistician for several clinical and basic science studies of human infectious and immunologic disease.
Dr. Shaw is an adjunct associate professor in the Department of Biostatistics, Epidemiology and Informatics at the University of Pennsylvania and an affiliate professor in the Department of Biology and Wildlife at the University of Alaska Fairbanks. She is associate editor for Statistics in Medicine. She serves as a member for several clinical trial data safety monitoring boards and as a member of the Bone, Reproductive and Urologic Drugs Advisory Committee for the U.S. Food and Drug Administration. She is a member of the International Biometric Society and fellow of the American Statistical Association.
She completed a Bachelor of Arts in mathematics and French at Grinnell College, and a Master of Science in mathematics and a doctorate in biostatistics at the University of Washington.
Han K, Shaw PA, Lumley T. Combining multiple imputation with raking of weights in the setting of nearly-true models. Stat Med. 2021 Dec 30;40(30):6777-6791. doi: 10.1002/sim.9210. Epub 2021 Sep 28. PubMed
Lotspeich SC, Shepherd BE, Amorim GGC, Shaw PA, Tao R. Efficient odds ratio estimation under two-phase sampling using error-prone data from a multi-national HIV research cohort. Biometrics. 2022;78(4):1674-1685. doi: 10.1111/biom.13512. Epub 2021 Aug 1. PubMed
Baldoni P, Sotres-Alvarez D, Lumley TS, Shaw PA. On the use of regression calibration in a complex sampling design with application to the Hispanic community health study/study of Latinos. Am J Epidemiol. 2021 Jul 1;190(7):1366-1376. doi: 10.1093/aje/kwab008. PubMed
Boe LA, Tinker LF, Shaw PA. An approximate quasi-likelihood approach for error-prone failure time outcomes and exposures. Stat Med. 2021 Oct 15;40(23):5006-5024. doi: 10.1002/sim.9108. Epub 2021 Jun 22. PubMed
Amorim G, Tao R, Lotspeich S, Shaw PA, Lumley T, Shepherd BE. Two-phase sampling designs for data validation in settings with covariate measurement error and continuous outcome. J R Stat Soc Ser A Stat Soc. 2021 Oct;184(4):1368-1389. doi: 10.1111/rssa.12689. Epub 2021 Apr 15. PubMed
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