Risk Factors and Interventions for HIV Control in China

__Abstract__ Acquired immune deficiency syndrome (AIDS) is caused by the human immunodefiency virus (HIV) and has been recognized as a major public health problem for many years. At the end of 2012, approximately 34 million people were living with HIV globally. The worldwide prevalence is approximately 0.8% in adults aged 15- 49 years. The burden of the epidemic varies considerably between countries and regions. Sub-Saharan Africa is the most severely affected area (Figure 1.1), with a prevalence of about 5% in adults and accounting for 69% of all people with HIV infection worldwide.2 With 48,000 new HIV cases in the year 2011, it is estimated that there are 780,000 people living with HIV/AIDS in China nowadays. HIV was first identified by Luc Montagnier in 1983

Nonparametric inference procedure for percentiles of the random effects distribution in meta-analysis

To investigate whether treating cancer patients with erythropoiesis-stimulating agents (ESAs) would increase the mortality risk, Bennett et al. [Journal of the American Medical Association 299 (2008) 914--924] conducted a meta-analysis with the data from 52 phase III trials comparing ESAs with placebo or standard of care. With a standard parametric random effects modeling approach, the study concluded that ESA administration was significantly associated with increased average mortality risk. In this article we present a simple nonparametric inference procedure for the distribution of the random effects. We re-analyzed the ESA mortality data with the new method. Our results about the center of the random effects distribution were markedly different from those reported by Bennett et al. Moreover, our procedure, which estimates the distribution of the random effects, as opposed to just a simple population average, suggests that the ESA may be beneficial to mortality for approximately a quarter of the study populations. This new meta-analysis technique can be implemented with study-level summary statistics. In contrast to existing methods for parametric random effects models, the validity of our proposal does not require the number of studies involved to be large. From the results of an extensive numerical study, we find that the new procedure performs well even with moderate individual study sample sizes.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS280 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org