Projects

A bird in a tree
Dr. Gilbert at work
Dr. Gilbert at work in his office

The Gilbert Group’s research program centers on the statistical design and analysis of vaccine efficacy trials, especially HIV Vaccine Trials Network (HVTN) trials of candidate HIV vaccines, but also trials of candidate vaccines against other genetically-diverse infectious diseases including dengue, malaria, and TB. The research integrates novel statistical methods with applications, with major application area the assessment of “immune correlates of vaccine protection” including the “sieve analysis” of pathogen sequences infecting trial volunteers, a statistical field that Dr. Gilbert pioneered and led development with colleagues. The novel methods research contributes to general biostatistical fields including competing risks survival analysis, missing data (e.g., two-phase sampling designs), causal inference (e.g., principal stratification and mediation), and the evaluation of surrogate endpoints; the methods focus on robust and efficient techniques including targeted learning methods that integrate machine learning into inferences. Closely connected to its HVTN research on HIV vaccines, the Gilbert Group also focuses on HVTN efficacy trials of broadly neutralizing monoclonal antibodies for prevention of HIV infection. The Gilbert Group conducts the research through extensive collaboration with University of Washington biostatistics doctoral and masters students.

Current Research

  • PI for Statistical Data Management Center for the HIV Vaccine Trials Network (HVTN).
  • Statistician for HVTN HIV vaccine and broadly neutralizing monoclonal antibody clinical trials.
  • Development of statistical methods integrated with applied statistical research for assessment of immune correlates of protection, genomic sieve analysis and other aspects of assessing vaccine immunogenicity and efficacy for HIV and other genetically diverse pathogens (including dengue, influenza, malaria, rotavirus, RSV, TB).
  • Development of statistical methods for survival analysis, causal inference, and machine learning in randomized clinical trials.