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1.  Who will Benefit from a Wide-Scale Introduction of Vaginal Microbicides in Developing Countries?* 
Vaginal microbicides (VMB) are currently among the few biomedical interventions designed to help women reduce their risk of acquiring HIV infection. However, the microbicide containing antiretroviral (ARV-VMB) may lead to the development of antiretroviral resistance and could paradoxically become more beneficial to men at the population level.
We developed a mathematical model to study the impact of a wide-scale population usage of VMB in a heterosexual population. Gender ratios of prevented infections and prevalence reduction are evaluated in 63 different intervention schedules including continuous and interrupted ARV-VMB use by HIV-positive women. The influence of different factors on population-level benefits is also studied through Monte Carlo simulations using parameters sampled from primary ranges representative of developing countries.
Our analysis indicates that women are more likely than men to benefit from ARV-VMB use since 78-80% of the total 63,000 simulations investigated (under different parameter sets) showed a female advantage whether benefit is measured as cumulative number of infections prevented, the percentage of cumulative infections prevented, or the expected reduction in prevalence. Stratified analysis by scenarios indicates that the likelihood of a male advantage with respect to the fractions of prevented infections varies from 6% to 49% among the scenarios. It is substantial only if the risk of systemic absorption and development of resistance to ARV-VMB is high and the HIV-positive women use VMB indefinitely without interruption. Therefore, the use of ARV-VMB, with successful control measures restricting usage by HIV-positive women, is still very much a female prevention tool.
PMCID: PMC3905618  PMID: 24490001
mathematical model; HIV transmission; HIV prevention; vaginal microbicide
2.  Episodic HIV Risk Behavior Can Greatly Amplify HIV Prevalence and the Fraction of Transmissions from Acute HIV Infection 
A deterministic compartmental model was explored that relaxed the unrealistic assumption in most HIV transmission models that behaviors of individuals are constant over time. A simple model was formulated to better explain the effects observed. Individuals had a high and a low contact rate and went back and forth between them. This episodic risk behavior interacted with the short period of high transmissibility during acute HIV infection to cause dramatic increases in prevalence as the differences between high and low contact rates increased and as the duration of high risk better matched the duration of acute HIV infection. These same changes caused a considerable increase in the fraction of all transmissions that occurred during acute infection. These strong changes occurred despite a constant total number of contacts and a constant total transmission potential from acute infection. Two phenomena played a strong role in generating these effects. First, people were infected more often during their high contact rate phase and they remained with high contact rates during the highly contagious acute infection stage. Second, when individuals with previously low contact rates moved into an episodic high-risk period, they were more likely to be susceptible and thus provided more high contact rate susceptible individuals who could get infected. These phenomena make test and treat control strategies less effective and could cause some behavioral interventions to increase transmission. Signature effects on genetic patterns between HIV strains could make it possible to determine whether these episodic risk effects are acting in a population.
PMCID: PMC3778933  PMID: 24058722
HIV; mathematical modeling; risk behavior; MSM
3.  Sample Size for a Binomial Proportion with Autocorrelation 
A flexible sample size computation is desired for a binomial outcome consisting of repeated binary measures with autocorrelation over time. This type of outcome is common in viral shedding studies, in which each individual’s outcome is a proportion: the number of samples on which virus is detected out of number of samples assessed. Autocorrelation between proximal samples occurs in some conditions such as herpes infection, in which reactivation is episodic. We determine a sample size computation that accounts for: (1) participant-level differences in outcome frequency, (2) autocorrelation in time between samples, and (3) varying number of samples per participant. In addition, we develop a computation appropriate for crossover designs that accounts for the dependence of the investigational treatment effect on the pretreatment detection frequency. The computations are validated through comparison with real and simulated data, and sensitivity to misspecification of parameter values is examined graphically.
PMCID: PMC3590028  PMID: 23476723
4.  A Sequential Phase 2b Trial Design for Evaluating Vaccine Efficacy and Immune Correlates for Multiple HIV Vaccine Regimens 
Five preventative HIV vaccine efficacy trials have been conducted over the last 12 years, all of which evaluated vaccine efficacy (VE) to prevent HIV infection for a single vaccine regimen versus placebo. Now that one of these trials has supported partial VE of a prime-boost vaccine regimen, there is interest in conducting efficacy trials that simultaneously evaluate multiple prime-boost vaccine regimens against a shared placebo group in the same geographic region, for accelerating the pace of vaccine development. This article proposes such a design, which has main objectives (1) to evaluate VE of each regimen versus placebo against HIV exposures occurring near the time of the immunizations; (2) to evaluate durability of VE for each vaccine regimen showing reliable evidence for positive VE; (3) to expeditiously evaluate the immune correlates of protection if any vaccine regimen shows reliable evidence for positive VE; and (4) to compare VE among the vaccine regimens. The design uses sequential monitoring for the events of vaccine harm, non-efficacy, and high efficacy, selected to weed out poor vaccines as rapidly as possible while guarding against prematurely weeding out a vaccine that does not confer efficacy until most of the immunizations are received. The evaluation of the design shows that testing multiple vaccine regimens is important for providing a well-powered assessment of the correlation of vaccine-induced immune responses with HIV infection, and is critically important for providing a reasonably powered assessment of the value of identified correlates as surrogate endpoints for HIV infection.
PMCID: PMC3502884  PMID: 23181167
HIV vaccine efficacy clinical trial; immune correlate of protection; one-way crossover design; surrogate endpoint for HIV infection; two-phase sampling
5.  Prediction of an Epidemic Curve: A Supervised Classification Approach 
Classification methods are widely used for identifying underlying groupings within datasets and predicting the class for new data objects given a trained classifier. This study introduces a project aimed at using a combination of simulations and classification techniques to predict epidemic curves and infer underlying disease parameters for an ongoing outbreak.
Six supervised classification methods (random forest, support vector machines, nearest neighbor with three decision rules, linear and flexible discriminant analysis) were used in identifying partial epidemic curves from six agent-based stochastic simulations of influenza epidemics. The accuracy of the methods was compared using a performance metric based on the McNemar test.
The findings showed that: (1) assumptions made by the methods regarding the structure of an epidemic curve influences their performance i.e. methods with fewer assumptions perform best, (2) the performance of most methods is consistent across different individual-based networks for Seattle, Los Angeles and New York and (3) combining classifiers using a weighting approach does not guarantee better prediction.
PMCID: PMC3445421  PMID: 22997545
epidemic curves; supervised learning; agent-based epidemic models; classification; random forest
6.  An imputation method for interval censored time-to-event with auxiliary information: analysis of the timing of mother-to-child transmission of HIV 
The timing of mother-to-child transmission (MTCT) of HIV is critical in understanding the dynamics of MTCT. It has a great implication to developing any effective treatment or prevention strategies for such transmissions. In this paper, we develop an imputation method to analyze the censored MTCT timing in presence of auxiliary information. Specifically, we first propose a statistical model based on the hazard functions of the MTCT timing to reflect three MTCT modes: in utero, during delivery and via breastfeeding, with different shapes of the baseline hazard that vary between infants. This model also allows that the majority of infants may be immuned from the MTCT of HIV. Then, the model is fitted by MCMC to explore marginal inferences via multiple imputation. Moreover, we propose a simple and straightforward approach to take into account the imperfect sensitivity in imputation step, and study appropriate censoring techniques to account for weaning. Our method is assessed by simulations, and applied to a large trial designed to assess the use of antibiotics in preventing MTCT of HIV.
PMCID: PMC3419597  PMID: 22905281
HIV/AIDS; mixture models; mother to child transmission of HIV; multiple imputation
7.  Nonparametric and Semiparametric Analysis of Current Status Data Subject to Outcome Misclassification 
In this article, we present nonparametric and semiparametric methods to analyze current status data subject to outcome misclassification. Our methods use nonparametric maximum likelihood estimation (NPMLE) to estimate the distribution function of the failure time when sensitivity and specificity are known and may vary among subgroups. A nonparametric test is proposed for the two sample hypothesis testing. In regression analysis, we apply the Cox proportional hazard model and likelihood ratio based confidence intervals for the regression coefficients are proposed. Our methods are motivated and demonstrated by data collected from an infectious disease study in Seattle, WA.
PMCID: PMC3298195  PMID: 22408713
Current status data; Outcome misclassification; Sensitivity; Specificity
8.  Increasing the Efficiency of Prevention Trials by Incorporating Baseline Covariates 
Most randomized efficacy trials of interventions to prevent HIV or other infectious diseases have assessed intervention efficacy by a method that either does not incorporate baseline covariates, or that incorporates them in a non-robust or inefficient way. Yet, it has long been known that randomized treatment effects can be assessed with greater efficiency by incorporating baseline covariates that predict the response variable. Tsiatis et al. (2007) and Zhang et al. (2008) advocated a semiparametric efficient approach, based on the theory of Robins et al. (1994), for consistently estimating randomized treatment effects that optimally incorporates predictive baseline covariates, without any parametric assumptions. They stressed the objectivity of the approach, which is achieved by separating the modeling of baseline predictors from the estimation of the treatment effect. While their work adequately justifies implementation of the method for large Phase 3 trials (because its optimality is in terms of asymptotic properties), its performance for intermediate-sized screening Phase 2b efficacy trials, which are increasing in frequency, is unknown. Furthermore, the past work did not consider a right-censored time-to-event endpoint, which is the usual primary endpoint for a prevention trial. For Phase 2b HIV vaccine efficacy trials, we study finite-sample performance of Zhang et al.'s (2008) method for a dichotomous endpoint, and develop and study an adaptation of this method to a discrete right-censored time-to-event endpoint. We show that, given the predictive capacity of baseline covariates collected in real HIV prevention trials, the methods achieve 5-15% gains in efficiency compared to methods in current use. We apply the methods to the first HIV vaccine efficacy trial. This work supports implementation of the discrete failure time method for prevention trials.
PMCID: PMC2997740  PMID: 21152074
Auxiliary; Covariate Adjustment; Intermediate-sized Phase 2b Efficacy Trial; Semiparametric Efficiency

Results 1-8 (8)