Effect of tuberculosis on immune restoration among HIV-infected patients receiving antiretroviral therapy

In this article, time to immune recovery during antiretroviral therapy was estimated and compared between HIV-infected children with and without tuberculosis (TB). CD4?T-cell restoration was used as a criterion for determining immune recovery. The median residual lifetime function, which is more intuitive and robust compared to the frequently used measures of lifetime data, was used to estimate time to CD4?T-cell restoration. The median residual lifetime is not influenced by extreme observations and heavy-tailed distributions which are commonly encountered in clinical studies. Permutation-based methods were used to compare the CD4?T-cell restoration times between the two groups of patients. Our results indicate that children with TB had uniformly higher median residual lifetimes to immune recovery compared to those without TB. Although TB was associated with slower CD4?T-cell restoration, the differences between the restoration times of the two groups were not statistically significant.     

Nonparametric estimation of mean residual functions

Situations frequently arise in practice in which mean residual life (mrl) functions must be ordered. For example, in a clinical trial of three experiments, let e (1), e (2) and e (3) be the mrl functions, respectively, for the disease groups under the standard and experimental treatments, and for the disease-free group. The well-documented mrl functions e (1) and e (3) can be used to generate a better estimate for e (2) under the mrl restriction e (1) < or = e (2) < or = e (3). In this paper we propose nonparametric estimators of the mean residual life function where both upper and lower bounds are given. Small and large sample properties of the estimators are explored. Simulation study shows that the proposed estimators have uniformly smaller mean squared error compared to the unrestricted empirical mrl functions. The proposed estimators are illustrated using a real data set from a cancer clinical trial study.     

Joint modeling of correlated binary outcomes: HIV-1 and HSV-2 co-infection

Herpes Simplex Virus Type 2 (HSV-2) facilitates the sexual acquisition and transmission of HIV-1 infection and is highly prevalent in most regions experiencing severe HIV epidemics. In sub-Saharan Africa, where HIV infection is a public health burden, the prevalence of HSV-2 is substantially high. The high prevalence of HSV-2 and the association between HSV-2 infection and HIV-1 acquisition could play a significant role in the spread of HIV-1 in the region. The objective of our study was to identify risk factors for HSV-2 and HIV-1 infections among men in sub-Saharan Africa. We used a joint response model that accommodates the interdependence between the two infections in assessing their risk factors. Simulation studies show superiority of the joint response model compared to the traditional models which ignore the dependence between the two infections. We found higher odds of having HSV-2/HIV-1 among older men, in men who had multiple sexual partners, abused alcohol, or reported symptoms of sexually transmitted infections. These findings suggest that interventions that identify and control the risk factors of the two infections should be part of HIV-1 prevention programs in sub-Saharan Africa where antiretroviral therapy is not readily available.