Jingyang Zhang of the Fred Hutchinson Cancer Research Center will present on Thursday, October 8, at 4:10 p.m. in Neill 5W. Refreshments will be served at 3:30 p.m. in Neill 216.


HIV has become one of the world’s most serious health and development challenges. Numerous new preventive interventions are currently under investigation. Estimating the effectiveness of a new intervention is usually the primary objective for HIV prevention trials. The Cox proportional hazard model is mainly used to estimate effectiveness by assuming that participants share the same risk under the covariates and the risk is always non-zero. In fact, the risk is only non-zero when an exposure event occurs, and participants can have a varying risk to transmit due to varying patterns of exposure events. In this talk, we will provide an alternative perspective to assess a candidate intervention by examining the effectiveness at an individual exposure to HIV. The individual-level effectiveness accounts for the heterogeneity in the magnitude of exposure among study participants, using a latent Poisson process model. It also allows that a proportion of participants never experience an exposure event by adopting a zero-inflated distribution for the rate of the exposure process. We propose a Bayesian hierarchical model to estimate the individual-level effectiveness eliciting the priors from the historical information. An application example is presented from an HIV prevention trial.