AIDS: Prophecy and Present Reality

Mathematical modeling of the AIDS epidemic can be useful for policymakers even though precise projections are not possible at this time. Models are useful in establishing ranges for current and future prevalence of HIV infection and incidence of AIDS, as well as in predicting the effect of a given intervention strategy. Most decision makers are using models implicitly when they use epidemiological information as a basis for policy; formulating a model explicitly permits examination of the underlying assumptions. By creating and testing a variety of models, an investigator can determine whether the models reflect more the underlying assumptions or the available data. Modeling is a process that helps the policymaker test and refine his or her own beliefs about the future of the epidemic and the effect of behavioral intervention. In this report, the process is examined in relation to five policy problems posed by the AIDS epidemic

CRTgeeDR: An R Package for Doubly Robust Generalized Estimating Equations Estimations in Cluster Randomized Trials with Missing Data

International audienceSemi-parametric approaches based on generalized estimating equation (GEE) are widelyused to analyse correlated outcomes. Most available softwares had been developed forlongitudinal settings. In this paper, we present a R package CRTgeeDR for estimatingparameters in marginal regression in cluster randomized trials (CRTs). Theory for adjustingfor missing at random outcomes by inverse-probability weighting methods (IPW)based on the use of a propensity score had been largely studied and implemented. Weexhibit that in CRTs most of the available softwares use an implementation of weightsthat lead to a bias in estimation if a non-independence working correlation structure ischosen. In CRTgeeDR, we solve this problem by using a different implementation whilekeeping the consistency properties of the IPW. Moreover, in CRTs using an augmentedGEE (AUG) allow to improve efficiency by adjusting for treatment-covariate interactionsand imbalance in baseline covariates between treatment groups using an outcome model.In CRTgeeDR, we extend the abilities of existing packages such as geepack and geeMto allow such data augmentation. Finally, one may want to combine IPW and AUG ina Doubly Robust (DR) estimator, which lead to consistent estimation when either thepropensity score or the outcome model corresponds to the true data generation process(Prague, Wang, Stephens, Tchetgen Tchetgen, and De gruttola 2015). The DR approachis implemented in CRTgeeDR. Simulations studies demonstrate the consistency of IPWimplemented in CRTgeeDR and the gains associated with the use of the DR for analyzinga binary outcome using a logit regression. Finally, we reanalyzed data from a sanitationCRT in developing countries (Guiteras, Levinsohn, and Mobarak 2015a) with the DRapproach compared to classical GEE and demonstrated a signiffcant intervention effect

Bayesian Estimation of Mixture Models with Prespecified Elements to Compare Drug Resistance in Treatment-Naïve and Experienced Tuberculosis Cases

We propose a Bayesian approach for estimating branching tree mixture models to compare drug-resistance pathways (i.e. patterns of sequential acquisition of resistance to individual antibiotics) that are observed among Mycobacterium tuberculosis isolates collected from treatment-naïve and treatment-experienced patients. Resistant pathogens collected from treatment-naïve patients are strains for which fitness costs of resistance were not sufficient to prevent transmission, whereas those collected from treatment-experienced patients reflect both transmitted and acquired resistance, the latter of which may or may not be associated with lower transmissibility. The comparison of the resistance pathways constructed from these two groups of drug-resistant strains provides insight into which pathways preferentially lead to the development of multiple drug resistant strains that are transmissible. We apply the proposed statistical methods to data from worldwide surveillance of drug-resistant tuberculosis collected by the World Health Organization over 13 years