This study gave the first estimates of HSV-2 per-sex-act FtoMTPs in Africa. It demonstrated a synergy between HIV and HSV-2 infections and a protective effect of male circumcision on HSV-2 acquisition by males.

No abstract

Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, that double assumption is unlikely to hold, particularly for the random effects, a crucial component in which assessment of magnitude is key in such modeling. Alternative fitting methods not relying on that assumption (as ANOVA ones and Rao's MINQUE) apply, quite often, only to the very constrained class of variance components models. In this paper, a new computationally feasible estimation methodology is designed, first for the widely used class of 2-level (or longitudinal) LMMs with only assumption (beyond the usual basic ones) that residual errors are uncorrelated and homoscedastic, with no distributional assumption imposed on the random effects. A major asset of this new approach is that it yields nonnegative variance estimates and covariance matrices estimates which are symmetric and, at least, positive semi-definite. Furthermore, it is shown that when the LMM is, indeed, Gaussian, this new methodology differs from ML just through a slight variation in the denominator of the residual variance estimate. The new methodology actually generalizes to LMMs a well known nonparametric fitting procedure for standard Linear Models. Finally, the methodology is also extended to ANOVA LMMs, generalizing an old method by Henderson for ML estimation in such models under normality.

In the estimation of a lifetime distribution from regular interval-censored data with an additional censoring variable, we focus on the case where (contrary to the actuarial method) both events (interest and censorship) can occur on a given individual in the same interval and, thus, are observed simultaneously. Specifically, we consider a population where individuals pass through a finite number of successive stages during their growth and are threatened by a disease. First, we estimate the lifetime and time-to-disease distributions in each developmental stage from such censored data. Using data that were recorded on a cohort of individuals followed over a long period of time, we propose a non-parametric, yet continuously differentiable and piecewise quadratic polynomial, estimator for the survival function of each of these distributions. We applied it to estimate, from weekly field observations in Mbankomo (Cameroon), the lifetime and time-to-disease distributions of cocoa fruits in each of their three developmental stages before maturity. It is found that on average a healthy cocoa fruit spends only 2 1 2 weeks in its first stage (cherelle), compared with nearly 9 weeks as a young pod and 7 1 2 weeks as an adult pod. In a second phase, however, adapting our methodology to competing risks estimation, we observed that, owing to the severe rate of attacks, the fruits' effective lifetime expectancy in farmland is much shorter. Indeed, in that part of Cameroon, the cumulative risk of an attack on cocoa fruits in farmland, especially by pod rot disease, far outweighs their chances of reaching maturity.