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Objective: To compare the number and type of nevirapine (NVP) resistance mutations detected in Ugandan women with subtype A vs. D HIV-1 infection after single-dose NVP prophylaxis., Design: In the HIVNET 012 trial, a higher rate of NVP resistance (NVPR) was seen in women with subtype D than A after single-dose NVP. In this study, the number and type of NVPR mutations detected 6-8 weeks after NVP were compared in women with subtypes A vs. D., Methods: Plasma samples were available for 282 (92%) of 306 women who received NVP in HIVNET 012. Samples were analyzed with the ViroSeq HIV-1 Genotyping System (Applied Biosystems, Foster City, CA). Subtyping was performed by phylogenetic analysis of pol region sequences., Results: Results were obtained for 279 women, including 147 with subtype A, 98 with subtype D, 6 with subtype C, and 28 with recombinant HIV-1. NVPR mutations were detected in 70 (25%) of 279 women. NVPR was more common in women with subtype D vs. A (35.7 vs. 19%, P = 0.0035). Complex patterns of NVPR mutations were detected in both subtypes. Among women with NVPR, 43% of women with subtype A and 46% of women with subtype D had >/=2 NVPR mutations. The mean number and pattern of NVPR mutations detected in women with subtypes A and D were similar., Conclusions: This study confirms a higher rate of NVPR in women with subtype D than A and further defines the pattern of NVPR mutations that emerge 6-8 weeks after single-dose NVP prophylaxis in these subtypes.

Complex issues arise when investigating the association between longitudinal immunologic measures and time to an event, such as time to relapse, in cancer vaccine trials. Unlike many clinical trials, we may encounter patients who are cured and no longer susceptible to the time-to-event endpoint. If there are cured patients in the population, there is a plateau in the survival function, S(t), after sufficient follow-up. If we want to determine the association between the longitudinal measure and the time-to-event in the presence of cure, existing methods for jointly modeling longitudinal and survival data would be inappropriate, since they do not account for the plateau in the survival function. The nature of the longitudinal data in cancer vaccine trials is also unique, as many patients may not exhibit an immune response to vaccination at varying time points throughout the trial. We present a new joint model for longitudinal and survival data that accounts both for the possibility that a subject is cured and for the unique nature of the longitudinal data. An example is presented from a cancer vaccine clinical trial.

This article proposes a new semiparametric Bayesian hierarchical model for the joint modeling of longitudinal and survival data. We relax the distributional assumptions for the longitudinal model using Dirichlet process priors on the parameters defining the longitudinal model. The resulting posterior distribution of the longitudinal parameters is free of parametric constraints, resulting in more robust estimates. This type of approach is becoming increasingly essential in many applications, such as HIV and cancer vaccine trials, where patients' responses are highly diverse and may not be easily modeled with known distributions. An example will be presented from a clinical trial of a cancer vaccine where the survival outcome is time to recurrence of a tumor. Immunologic measures believed to be predictive of tumor recurrence were taken repeatedly during follow-up. We will present an analysis of this data using our new semiparametric Bayesian hierarchical joint modeling methodology to determine the association of these longitudinal immunologic measures with time to tumor recurrence.