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.
Hughes JP, Williamson BD, Krakauer C, Chau G, Ortiz B, Wakefield J, Hendrix C, Amico KR, Holtz TH, Bekker LG, Grant R. Combining information to estimate adherence in studies of pre-exposure prophylaxis for HIV prevention: Application to HPTN 067. LID - 10.1002/sim.9321 [doi] Stat Med. 2022 Jan 25. doi: 10.1002/sim.9321 [Epub ahead of print] PubMed
Huang Y, Williamson BD, Moodie Z, Carpp LN, Chambonneau L, DiazGranados CA, Gilbert PB. Analysis of neutralizing antibodies as a correlate of instantaneous risk of hospitalized dengue in placebo recipients of dengue vaccine efficacy trials. J Infect Dis. 2021 Jun 26:jiab342. doi: 10.1093/infdis/jiab342. Online ahead of print. PubMed
Williamson BD, Hughes JP, Willis AD. A multiview model for relative and absolute microbial abundances. Biometrics. 2021 May 28. doi: 10.1111/biom.13503. Online ahead of print. PubMed
Williamson BD, Magaret CA, Gilbert PB, Nizam S, Simmons C, Benkeser D. Super LeArner Prediction of NAb Panels (SLAPNAP): a containerized tool for predicting combination monoclonal broadly neutralizing antibody sensitivity. Bioinformatics. 2021 May 22;btab398. doi: 10.1093/bioinformatics/btab398. Online ahead of print. PubMed
Duke ER, Williamson BD, Borate B, Golob JL, Wychera C, Stevens-Ayers T, Huang ML, Cossrow N, Wan H, Mast TC, Marks MA, Flowers ME, Jerome KR, Corey L, Gilbert PB, Schiffer JT, Boeckh M. Cytomegalovirus viral load kinetics as surrogate endpoints after allogeneic transplantation. J Clin Invest. 2021 Jan 4;131(1):e133960. doi: 10.1172/JCI133960. PubMed
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.