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1.  Estimating the Effectiveness in HIV Prevention Trials by Incorporating the Exposure Process: Application to HPTN 035 Data 
Biometrics  2014;70(3):742-750.
Summary
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. Therefore, we propose a novel estimate of effectiveness adjusted for the heterogeneity in the magnitude of exposure among the study population, using a latent Poisson process model for the exposure path of each participant. Moreover, our model considers the scenario in which a proportion of participants never experience an exposure event and adopts a zero-inflated distribution for the rate of the exposure process. We employ a Bayesian estimation approach to estimate the exposure-adjusted effectiveness eliciting the priors from the historical information. Simulation studies are carried out to validate the approach and explore the properties of the estimates. An application example is presented from an HIV prevention trial.
doi:10.1111/biom.12183
PMCID: PMC4239192  PMID: 24845658
Hierarchical models; HIV prevention; Intercourse; Markov chain Monte Carlo; Per-exposure effectiveness; Zero-inflated gamma
2.  Bayesian estimation of the time-varying sensitivity of a diagnostic test with application to mother-to-child transmission of HIV 
Biometrics  2010;66(4):1266-1274.
Summary
We present a Bayesian model to estimate the time-varying sensitivity of a diagnostic assay when the assay is given repeatedly over time, disease status is changing and the gold standard is only partially observed. The model relies on parametric assumptions for the distribution of the latent time of disease onset and the time-varying sensitivity. Additionally, we illustrate the incorporation of historical data for constructing prior distributions. We apply the new methods to data collected in a study of mother-to-child transmission of HIV and include a covariate for sensitivity to assess whether two different assays have different sensitivity profiles.
doi:10.1111/j.1541-0420.2010.01398.x
PMCID: PMC2940984  PMID: 20222936
Bayesian models; mother-to-child transmission of HIV; Time-varying sensitivity

Results 1-2 (2)