Are plasma levels valid surrogates for cellular concentrations of antiretroviral drugs in HIV-infected patients?

Total plasma concentrations are currently measured for therapeutic drug monitoring of HIV protease inhibitors (PIs) and nonnucleoside reverse transcriptase inhibitors (NNRTIs). However, the pharmacological target of antiretroviral drugs reside inside cells. To study the variability of their cellular accumulation, and to determine to which extent total plasma concentrations (TPC) correlate with cellular concentrations (CC), plasma and peripheral blood mononuclear cells (PBMCs) were simultaneously collected at single random times after drug intake from 133 HIV infected patients. TPC levels were analysed by high-performance liquid chromatography with ultraviolet detection and CC by LC-MS/MS from peripheral blood mononuclear cells. The best correlations between TPC and CC were observed for nelfinavir (NFV, slope=0.93, r=0.85), saquinavir (SQV, slope=0.76, r=0.80) and lopinavir (LPV, slope=0.87, r=0.63). By contrast, TPC of efavirenz (EFV) exhibited a moderate correlation with CC (slope=0.69, r=0.58), while no correlation was found for nevirapine (NVP, slope=-0.3, r=0.1). Interindividual variability in the CC/TPC ratio was lower for protease inhibitors (coefficients of variation 76%, 61%, and 80% for SQV, NFV and LPV, respectively) than for nonnucleoside reverse transcriptase inhibitors (coefficients of variation 101% and 318%, for EFV and NVP). As routine CC measurement raises practical difficulties, well-correlated plasma concentrations (ie, NFV, SQV and LPV) can probably be considered as appropriate surrogates for cellular drug exposure. For drugs such as EFV or NVP, there may be room for therapeutic drug monitoring improvement using either direct CC determination or other predictive factors such as genotyping of transporters or metabolizing enzyme genes.

Estimating the number of motor units using random sums with independently thinned terms

The problem of estimating the numbers of motor units N in a muscle is embedded in a general stochastic model using the notion of thinning from point process theory. In the paper a new moment type estimator for the numbers of motor units in a muscle is denned, which is derived using random sums with independently thinned terms. Asymptotic normality of the estimator is shown and its practical value is demonstrated with bootstrap and approximative confidence intervals for a data set from a 31-year-old healthy right-handed, female volunteer. Moreover simulation results are presented and Monte-Carlo based quantiles, means, and variances are calculated for N in{300,600,1000}.

Estimating the Number of Essential Genes in a Genome by Random Transposon Mutagenesis

We describe a Bayesian method for estimating the number of essential genes in a genome, on the basis of data on viable mutants for which a single transposon was inserted after a random TA site in a genome,potentially disrupting a gene. The prior distribution for the number of essential genes was taken to be uniform. A Gibbs sampler was used to estimate the posterior distribution. The method is illustrated with simulated data. Further simulations were used to study the performance of the procedure.

Posterior Simulation in the Generalized Linear Model with Semiparmetric Random Effects

Generalized linear mixed models with semiparametric random effects are useful in a wide variety of Bayesian applications. When the random effects arise from a mixture of Dirichlet process (MDP) model, normal base measures and Gibbs sampling procedures based on the Pólya urn scheme are often used to simulate posterior draws. These algorithms are applicable in the conjugate case when (for a normal base measure) the likelihood is normal. In the non-conjugate case, the algorithms proposed by MacEachern and Müller (1998) and Neal (2000) are often applied to generate posterior samples. Some common problems associated with simulation algorithms for non-conjugate MDP models include convergence and mixing difficulties. This paper proposes an algorithm based on the Pólya urn scheme that extends the Gibbs sampling algorithms to non-conjugate models with normal base measures and exponential family likelihoods. The algorithm proceeds by making Laplace approximations to the likelihood function, thereby reducing the procedure to that of conjugate normal MDP models. To ensure the validity of the stationary distribution in the non-conjugate case, the proposals are accepted or rejected by a Metropolis-Hastings step. In the special case where the data are normally distributed, the algorithm is identical to the Gibbs sampler.

The Importance of Scale for Spatial-confounding Bias and Precision of Spatial Regression Estimators

Increasingly, regression models are used when residuals are spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When the spatial residual is induced by an unmeasured confounder, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias depends on the spatial scales of the covariate and the residual; bias is reduced only when there is variation in the covariate at a scale smaller than the scale of the unmeasured confounding. I also discuss how the scales of the residual and the covariate affect efficiency and uncertainty estimation when the residuals can be considered independent of the covariate. In an application on the association between black carbon particulate matter air pollution and birth weight, controlling for large-scale spatial variation appears to reduce bias from unmeasured confounders, while increasing uncertainty in the estimated pollution effect.

The Optimal Confidence Region for a Random Parameter

Under a two-level hierarchical model, suppose that the distribution of the random parameter is known or can be estimated well. Data are generated via a fixed, but unobservable realization of this parameter. In this paper, we derive the smallest confidence region of the random parameter under a joint Bayesian/frequentist paradigm. On average this optimal region can be much smaller than the corresponding Bayesian highest posterior density region. The new estimation procedure is appealing when one deals with data generated under a highly parallel structure, for example, data from a trial with a large number of clinical centers involved or genome-wide gene-expession data for estimating individual gene- or center-specific parameters simultaneously. The new proposal is illustrated with a typical microarray data set and its performance is examined via a small simulation study.