Brian D Williamson

Brian D Williamson

he/him/his, biostatistician

Publications

For a complete list of publications, please see my Google Scholar page, PubMed, or ORCID.

Statistical Methodology

  1. Williamson BD and Huang Y. Flexible variable selection in the presence of missing data. International Journal of Biostatistics, 2024.
  2. Hughes JP, Williamson BD, Krakauer C, Chau G, Ortiz B, Wakefield J, Hendrix C, Amico KR, Holtz TH, Bekker LG, and Grant R. Combining information to estimate adherence in studies of pre-exposure prophylaxis for HIV prevention: application to HPTN 067. Statistics in Medicine, 2022.
  3. Williamson BD, Gilbert PB, Simon NR, and Carone M. A general framework for inference on algorithm-agnostic variable importance. Journal of the American Statistical Association, 2021.
  4. Williamson BD, Hughes JP, and Willis AD. A multi-view model for relative and absolute microbial abundances. Biometrics, 2021.
  5. Williamson BD, Gilbert PB, Carone M, and Simon N. Nonparametric variable importance assessment using machine learning techniques (with discussion). Biometrics, 2020.
  6. Williamson BD, Gilbert PB, Carone M, and Simon N. Rejoinder to “Nonparametric variable importance assessment using machine learning techniques”. Biometrics, 2020.
  7. Williamson BD and Feng J. Efficient nonparametric statistical inference on population feature importance using Shapley values. Proceedings of the 37th International Conference on Machine Learning, 2020.
  8. Hanscom B, Hughes JP, Williamson BD, and Donnel D. Adaptive non-inferiority margins under observable non-constancy. Statistical Methods in Medical Research, 2018.
  9. Feng J, Williamson BD, Carone M, and Simon N. Nonparametric variable importance using an augmented neural network with multi-task learning. Proceedings of the 35th International Conference on Machine Learning, 2018.
  10. Anchang B, Davis KL, Fienberg H, Williamson B, Bendall SC, Karacosta L, Tibshirani R, Nolan GP, and Plevritis SK. DRUG-NEM: optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity. Proceedings of the National Academy of Sciences, 2018.

Collaborative Science

  1. Williamson BD, King D, and Huang Y. Practical considerations for variable screening in the super learner. New England Journal of Statistics in Data Science, 2025.
  2. Cohen P, Lambson B, Mkhize N, Moodley C, Yssel A, Moyo-Gwete T, York T, Washu-nyangiwe A, Ndabambi N, Thebus R, Juraska M, deCamp A, Williamson B, Magaret C, Gilbert P, Westfall D, Deng W, Mullins J, Morris L, Williamson C, and Moore P. Resistance mutations that distinguish HIV-1 envelopes with discordant VRC01 phenotypes from multi-lineage infections in the HVTN703/HPTN081 trial: implications for cross-resistance. Journal of Virology, 2025.
  3. Wolock CJ, Williamson BD, Shortreed SM, Simon GE, Coleman KJ, Yeargans Y, Ahmedani BK, Daida Y, Lynch FL, Rossom RC, Ziebell RA, Cruz M, Wellman RD, and Coley RY. Importance of variable s from different time frames for predicting self-harm using health system data. Journal of Biomedical Informatics, 2024.
  4. Williamson BD, Wu L, Huang Y, Hudson A, and Gilbert PB. Predicting neutralization susceptibility to combination HIV-1 monoclonal broadly neutralizing antibody regimens. PLOS One, 2024.
  5. Navarro SL, Williamson BD, Huang Y, Gowda GAN, Raftery D, Tinker LF, Zheng C, Beresford SAA, Purcell H, Djukovic D, Gu H, Strickler HD, Tabung FK, Prentice RL, Neuhouser ML, and Lampe JW. Metabolite predictors of breast and colorectal cancer in the Womens’s Health Initiative. Metabolites, 2024.
  6. Phelan EA, Williamson BD, Balderson BH, Cook AJ, Piccorelli AV, Fujii MM, Nakata KG, Graham VF, Theis MK, Turner JP, Tannenbaum C, and Gray SL. Reducing central nervous system-active medications to prevent falls and injuries in older adults: a pragmatic, cluster-randomized controlled trial. JAMA Network Open, 2024.
  7. Carrell DC, Floyd JS, Gruber S, Hazlehurst BL, Heagerty PJ, Nelson JL, Williamson BD, and Ball R. A general framework for developing computable clinical phenotype algorithms. Journal of the American Medical Informatics Association, 2024.
  8. Magaret CA, Li L, deCamp AC, Rolland M, Juraska M, Williamson BD, Ludwig J, Molitor C, Benkeser D, Carpp LN, Greninger A, Roychoudhury P, Sadoff J, Gray GE, Vandebosch A, Grinsztejn B, Goepfert PA, Truyers C, Van Dromme I, Swann E, Marovich MA, Neuzil KM, Corey L, Hyrien O, and Gilbert PB. Quantifying how single dose Ad26.CoV2S vaccine efficacy depends on spike sequence features. Nature Communications, 2024.
  9. Juraska M, Bai H, deCamp AC, Magaret CA, Li L, Gillespie K, Carpp LN, Giorgi EE, Ludwig J, Molitor C, Hudson A, Williamson BD, Espy N, Simpkins B, Rudnicki E, Shao D, Rossenkhan R, Edlefsen P, Westfall DH, Deng W, Chen L, Zhao H, Bhattacharya T, Murrell B, Yssel A, Matten D, York T, Geaume N, Gwashu-Nyangiwe A, Ndabambi N, Thebus R, Karuna ST, Morris L, Montefiori DC, Hural JA, Cohen MS, Corey L, Rolland M, Gilbert PB, Williamson C, and Mullins JI. Prevention efficacy of the broadly neutralizing antibody VRC01 depends on HIV-1 envelope sequence features. Proceedings of the National Academy of Sciences, 2024.
  10. Hsu C, Williamson BD, Becker M, Berry B, Cook AJ, Derus A, Estrada C, Gacuiri M, Kone A, McCracken C, McDonald B, Piccorelli AV, Senturia K, Volney J, Wilson KB, and Green BB. Engaging staff to improve COVID-19 vaccination response at long-term care facilities (ENSPIRE): protocol for a cluster-randomized trial of co-designed, tailored vaccine promotion materials. Contemporary Clinical Trials, 2023.
  11. Smith JC, Williamson BD, Cronkite D, Park D, Whitaker JM, McLemore MF, Osmanski JT, Winter R, Ramaprasan A, Kelley A, Shea M, Wittayanukorn S, Stojanovic D, Zhao Y, Dutcher S, Toh D, Johnson KP, Aranoff DM, and Carrell DC. Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease. Journal of the American Medical Informatics Association, 2023.
  12. Williamson BD, Coley RY, Hsu C, McCracken CE, and Cook AJ. Considerations for subgroup analyses in cluster-randomized trials based on aggregated individual-level predictors. Prevention Science, 2023.
  13. Dang LE, Gruber S, Lee H, Dahabreh I, Stuart EA, Williamson BD, …, van der Laan MJ, and Petersen M. A causal roadmap for generating high-quality real-world evidence. Journal of Clinical and Translational Sciences, 2023.
  14. Williamson BD, Wyss R, Stuart EA, Dang LE, Mertens AN, Neugebauer RS, Wilson A, and Gruber S. An application of the causal roadmap in two safety monitoring case studies: Covariate-adjustement and outcome prediction using electronic health record data. Journal of Clinical and Translational Sciences, 2023.
  15. Williamson BD, Magaret CA, Karuna S, Carpp LN, Gelderblom HC, Huang Y, Benkeser D, and Gilbert PB. Application of the SLAPNAP statistical learning tool to broadly neutralizing antibody HIV prevention research. iScience, 2023.
  16. Balderson BH, Gray SL, Fujii MM, Nakata KG, Williamson BD, Cook AJ, Wellman R, Theis MK, Lewis CC, Key D, and 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.
  17. Benkeser D, …, Williamson BD, et al. Comparing and combining antibody assays as correlates of protection against COVID-19 in the COVE mRNA-1273 vaccine efficacy trial. Science Translational Medicine, 2023.
  18. Chen YT, Williamson BD, Okonek T, Wolock C, Spieker AJ, Hee Wai TY, Hughes JP, Emerson SS, and Willis AD. rigr: regression, inference and general data analysis tools in R. The Journal of Open Source Software, 2022.
  19. Huang Y, …, Williamson BD, McElrath MJ, Tomaras GD, Thakar J, Kobie J, et al. Baseline host determinants of robust human HIV-1 vaccine-induced immune responses: a meta-analysis of 26 vaccine regimens. eBioMedicine, 2022.
  20. Gilbert PB, …, and United States Government (USG)/CoVPN Biostatistics Team (incl. Williamson BD). Immune correlates analysis of the mRNA-1273 COVID-19 vaccine efficacy clinical trial. Science, 2021.
  21. Han S, Williamson BD, and Fong Y. Improving random forest predictions in small datasets from two-phase sampling designs. BMC Medical Informatics and Decision Making, 2021.
  22. Huang Y, Williamson BD, Moodie Z, Carpp LN, Chambonneau L, DiazGranados CA, and Gilbert PB. Analysis of neutralizing antibodies as a correlate of instantaneous risk of hospitalized dengue in placebo recipients of dengue vaccine efficacy trials. The Journal of Infectious Diseases, 2021.
  23. Williamson BD, Magaret CA, Gilbert PB, Nizam S, Simmons C, and Benkeser D. Super LeArner Prediction of NAb Panels (SLAPNAP): a containerized tool for predicting combination monoclonal broadly neutralizing antibody sensitivity. Bioinformatics, 2021.
  24. Duke ER, Williamson BD, Borate B, Golob JL, Wychera C, Stevens-Ayers T, Huang M-L, Cossrow N, Wan H, Mast CT, Marks MA, Flowers ME, Jerome KR, Corey L, Gilbert PB, Schiffer JT, and Boeckh MJ. Cytomegalovirus viral load kinetics as surrogate endpoints after allogeneic transplantation. The Journal of Clinical Investigation, 2020.
  25. Neidich SD, Fong Y, Li SS, Geraghty DE, Williamson BD, Young WC, Goodman D, Seaton KE, Shen X, Sawant S, Zhang L, deCamp AC, Blette BS, Shao M, Yates NL, Feely F, Pyo CW, Ferrari G, Frank I, Karuna ST, Swann E, Mascola J, Graham BS, Hammer SM, Sobieszczyk ME, Corey L, Janes H, McElrath MJ, Gottardo R, Gilbert PB, and Tomaras GD. Antibody Fc effector functions and IgG3 associate with decreased HIV-1 risk. The Journal of Clinical Investigation, 2019.
  26. Magaret CA, Benkeser DC, Williamson BDD, Borate BR, Carpp LN, Georgiev IS, Setliff I, Dingens AS, Simon N, Carone M, Simpkins C, Montefiori D, Alter G, Yu WH, Juraska M, Edlefsen PT, Karuna S, Mgodi NM, Edugupanti S, and Gilbert BP. Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 features. PLOS Computational Biology, 2019.
  27. Safren SA, Hughes JP, Mimiaga MJ, Moore AT, Friedman RK, Srithanaviboonchai K, Limbada M, Williamson BD, Elharrar V, Cummings V, Magidson JF, Gaydos CA, Celentano D, and Mayer KH for the HPTN063 Study Team. Frequency and predictors of estimated HIV transmissions and bacterial STI acquisition among HIV-positive patients in HIV care across three continents. Journal of the International AIDS Society, 2016.
  28. Ritchwood TD, Hughes JP, Jennings L, MacPhail C, Williamson B, Selin A, Kahn K, Gomes-Olive XF, and Pettifor A. Characteristics of age-discordant partnerships associated with HIV risk among young South African women (HPTN 068). Journal of Acquired Immune Deficiency Syndromes, 2016.