Bayesian evaluation of informative hypotheses for multiple populations

The software package Bain can be used for the evaluation of informative hypotheses with respect to the parameters of a wide range of statistical models. For pairs of hypotheses the support in the data is quantified using the approximate adjusted fractional Bayes factor (BF). Currently, the data have to come from one population or have to consist of samples of equal size obtained from multiple populations. If samples of unequal size are obtained from multiple populations, the BF can be shown to be inconsistent. This paper examines how the approach implemented in Bain can be generalized such that multiple-population data can properly be processed. The resulting multiple-population approximate adjusted fractional Bayes factor is implemented in the R package Bain.     

Avoidance as a predictor of the biological course of HIV infection over a 7-year period in gay men.

The present study prospectively investigated the relation between avoidance coping and active cognitive and behavioral coping and the progression of HIV infection over 7 years in 181 gay men. Findings revealed that for a number of medical and behavioral factors, (a) avoidance coping predicted a lower rate of decline in CD4 cells, (b) the proportional hazard (PH) attributable to avoidance of developing a syncytium-inducing HIV variant was 0.72 (95% confidence interval [CI]: 0.53 - 0.99, p < .05), and (c) the PH attributable to avoidance of dropping below 200 CD4 cells/microl was 0.66 (95% CI: 0.50 - 0.89, p < .01). Avoidance coping was not related to the development of AIDS-defining clinical symptoms. Active cognitive and behavioral coping was not related to the outcome measures.