Biomedical subjects
Publications and source records attributed to Jorge Alberto Achcar.
In this paper we propose the use of a multivariate null intercept measurement error model, where the true unobserved value of the covariate follows a mixture of two normal distributions. The proposed model is applied to a dental clinical trial presented in Hadgu and Koch (1999). A Bayesian approach is considered and a Gibbs Sampler is used to perform the computations.
Over the past two decades, nosocomial infections caused by extended-spectrum beta-lactamase (ESBL)-producing Klebsiella spp. have become a major problem all around the world. This situation is of concern because there are limited antimicrobial options to treat patients infected with these pathogens, and also because this kind of resistance can spread to a wide variety of Gram-negative bacilli. Our objectives were to evaluate among in-patients at a public university tertiary-care hospital with documented infection due to Klebsiella spp., which were the risk factors (cross-sectional analysis) and the clinical impact (prospective cohort) associated with an ESBL-producing strain. Study subjects were all patients admitted at the study hospital between April 2002 and October 2003, with a clinically and microbiologically confirmed infection caused by Klebsiella spp. at any body site, except infections restricted to the urinary tract. Of the 104 patients studied, 47 were infected with an ESBL-producing strain and 57 with a non-ESBL-producing strain. Independent risk factors associated with infection with an ESBL-producing strain were young age, exposure to mechanical ventilation, central venous catheter, use of any antimicrobial agent, and particularly use of a 4th generation cephalosporin or a quinolone. Length of stay was significant longer for patients infected with ESBL-producing strains than for those infected with non-ESBL-producing strains, although fatality rate was not significantly affected by ESBL-production in this cohort. In fact, mechanical ventilation and bacteremia were the only variables within dependent association with death detected in this investigation.
In many applications of lifetime data analysis, it is important to perform inferences about the mode of the hazard function in situations of lifetime data modeling with unimodal hazard functions. For lifetime distributions where the mode of the hazard function can be analytically calculated, its maximum likelihood estimator is easily obtained from the invariance properties of the maximum likelihood estimators. From the asymptotical normality of the maximum likelihood estimators, confidence intervals can be obtained. However, these results might not be very accurate for small sample sizes and/or large proportion of censored observations. Considering the log-logistic distribution for the lifetime data with shape parameter beta>1, we present and compare the accuracy of asymptotical confidence intervals with two confidence intervals based on bootstrap simulation. The alternative methodology of confidence intervals for the mode of the log-logistic hazard function are illustrated in three numerical examples.
The performance of a diagnostic test is usually summarized by its sensitivity and specificity. Sensitivity is the probability of a positive result, once the individual is truly ill, and specificity is the probability of a negative result, regarding a healthy individual. These measures are obtained by comparing the test outcome and the results of a reference test generically denominated gold standard. However, in many applied problems considering two diagnostic tests, the gold standard is not available for those individuals with negative results on both tests. In addition, not all diagnostic tests have the same performance across different populations. In this context, we present a Bayesian inference approach for performance measures estimation and we propose an extension of this procedure involving the inclusion of covariates. This Bayesian approach is based on Markov Chain Monte Carlo methods. The conditional dependence between the diagnostic tests was considered. As an example, we applied the proposed methodology to a real data set obtained from the medical literature.