Brian Williamson, PhD, is a biostatistician with expertise in statistical epidemiology, semiparametric and nonparametric estimation theory, and high-dimensional estimation and prediction. He is interested in developing robust procedures for statistical inference when machine learning is used to address problems in public health, and in working toward equity, diversity, and inclusion in biomedical research and practice. A central theme of his research is on identifying clinically useful biomarkers and assessing their performance.
Before joining Kaiser Permanente Washington Health Research Institute, Dr. Williamson completed his postdoctoral research training at the Fred Hutchinson Cancer Research Center. During his time at Fred Hutch, Dr. Williamson developed statistical methods to address issues arising in the development of biomarker panels for use in risk prediction, screening, and diagnosis. Dr. Williamson also collaborated with researchers from the Women’s Health Initiative to assess the utility of metabolomic biomarkers for predicting breast and colorectal cancer; with researchers from the HIV Vaccine Trials Network (HVTN) to aid in selecting candidate broadly neutralizing antibody regimens to advance to HIV prevention clinical trials; and was a part of the Coronavirus Prevention Network Biostatistics Team.
Dr. Williamson received his PhD in biostatistics from the University of Washington. His dissertation focused on a general framework for performing inference on model-free variable importance measures. With colleagues from the HVTN, he used this framework to identify features of the HIV viral genome that may be important in predicting viral susceptibility to the broadly neutralizing antibody VRC01.
At KPWHRI, Dr. Williamson collaborates on projects across a range of research areas including mental health, pragmatic clinical trials, and drug and vaccine safety and effectiveness.
Williamson BD, Coley RY, Hsu C, McCracken CE, Cook AJ. Considerations for subgroup analyses in cluster-randomized trials based on aggregated individual-level predictors. Prev Sci. 2023 Oct 28. doi: 10.1007/s11121-023-01606-1. [Epub ahead of print]. PubMed
Dang LE, Gruber S, Lee H, Dahabreh IJ, Stuart EA, Williamson BD, Wyss R, Díaz I, Ghosh D, Kiciman E, Alemayehu D, Hoffman KL, Vossen CY, Huml RA, Ravn H, Kvist K, Pratley R, Shih MC, Pennello G, Martin D, Waddy SP, Barr CE, Akacha M, Buse JB, van der Laan M, Petersen M. A causal roadmap for generating high-quality real-world evidence. J Clin Transl Sci. 2023 Sep 22;7(1):e212. doi: 10.1017/cts.2023.635. eCollection 2023. PubMed
Williamson BD, Wyss R, Stuart EA, Dang LE, Mertens AN, Neugebauer RS, Wilson A, Gruber S. An application of the Causal Roadmap in two safety monitoring case studies: Causal inference and outcome prediction using electronic health record data. J Clin Transl Sci. 2023 Sep 21;7(1):e208. doi: 10.1017/cts.2023.632. eCollection 2023. PubMed
Williamson BD, Magaret CA, Karuna S, Carpp LN, Gelderblom HC, Huang Y, Benkeser D, Gilbert PB. Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research. iScience. 2023 Aug 9;26(9):107595. doi: 10.1016/j.isci.2023.107595. eCollection 2023 Sep 15. PubMed
Balderson BH, Gray SL, Fujii MM, Nakata KG, Williamson BD, Cook AJ, Wellman R, Theis MK, Lewis CC, Key D, Phelan EA. A health-system-embedded deprescribing intervention targeting patients and providers to prevent falls in older adults (STOP-FALLS trial): Study protocol for a pragmatic cluster-randomized controlled trial. Trials. 2023 May 11;24(1):322. doi: 10.1186/s13063-023-07336-7. PubMed
KPWHRI receives $10 million to continue vaccine effectiveness research for flu, COVID-19, and other respiratory diseases.
Dr. Jennifer Nelson explains how KP scientists are helping the CDC and FDA keep an eye out for rare adverse events.
NIMH funding will enable the MHRN to conduct larger studies in integrated health systems on topics that matter most.