Josmar Mazucheli

The Lindley distribution applied to competing risks lifetime data

Competing risks data usually arises in studies in which the death or failure of an individual or an item may be classified into one of k ≥ 2 mutually exclusive causes. In this paper a simple competing risks distribution is proposed as a possible alternative to the Exponential or Weibull distributions usually considered in lifetime data analysis. We consider the case when the competing risks have a Lindley distribution. Also, we assume that the competing events are uncorrelated and that each subject can experience only one type of event at any particular time.

Pharmacodynamic evaluation of the potential clinical utility of fosfomycin and meropenem in combination therapy against KPC-2 producing Klebsiella pneumoniae

ABSTRACT KPC-producing Klebsiella pneumoniae causes serious infections associated with high death rates worldwide. Combination therapy consisting of fosfomycin and a carbapenem is better than monotherapy to combat multidrug-resistant microorganisms, but no dosages for the combination have been defined. The MICs of meropenem and fosfomycin were evaluated against 18 clinical isolates of KPC-2-producing K. pneumoniae. The activities of combination antimicrobials were also determined by the checkerboard method. The MIC50 and MIC90 of each agent alone and in combination were challenged against short (1.5-h) or prolonged (3-h) infusion regimens of meropenem (1 g every 8 h [q8h], 1.5 g q6h, 2 g q8h) and fosfomycin (4 g q8h, 6 g q6h, 8 g q8h) by Monte Carlo simulation to evaluate the time above the MIC of the free drug concentration as a percentage of the dosing interval (fT>MIC). The monotherapy MIC50s and MIC90s were 32 and 256 mg/liter for meropenem and 64 and 512 mg/liter for fosfomycin, respectively. Antimicrobial combination increased bacterial susceptibility to 1/4 the MIC50s and to 1/8 to 1/16 the MIC90s of monotherapy. The antimicrobial combination demonstrated a synergistic effect for at least two-thirds of the isolates. In combination therapy, fosfomycin regimens of 6 g q6h and 8 g q8h as a 3-h infusion against the MIC50 and MIC90 had better chances of achieving ≥90% probability of target attainment (PTA) of 70% fT>MIC. Meropenem regimens of 1.5 g q6h and 2 g q8h in prolonged infusion can achieve close to 90% PTA of 40% fT>MIC for MIC50 but not MIC90. The significant reduction in the MIC values and the achievement of appropriate PTA demonstrated that regimens containing fosfomycin with meropenem can be effective against KPC-2-producing K. pneumoniae.

Heterocedasticidade entre estados para produção de leite em vacas da raça Holandesa, usando métodos Bayesianos via amostrador de Gibbs

Adjusted for 305 days milk yield records of Holstein cows calving from 1980 to 1993 in the states of MG, SP, PR, SC, and RS were used to investigate heterogeneity of variance and to evaluate the genotype by environment interaction. Milk production from each State was considered as a different trait and variances were assumed heterogeneous. Milk production was also analyzed using a single-trait model assuming homogeneity of variance. (Co)variance components and genetic parameters were estimated by Bayesian inference, via Gibbs sampler (GS), using a model which included season of calving, genetic group, herd-year of calving and parity as fixed effects and animal additive genetic, permanent environmental and residual as random effects. Convergence of the GS chain to the stationary distribution was diagnosed using the method described by Heidelberg & Welch (1983). The posterior precision of the variance components and the heritability were high in the single-trait analysis. Posterior mean and standard deviation (SD) of heritability of milk yield were 0,278±0,012. For the multiple-trait analysis, posterior precisions of the (co)variance components were larger for SP and PR states. Posterior means and standard errors of heritability for MG, SP, PR, SC, and RS were 0.280±0.021, 0.233±0.015, 0.280±0.012, 0.393±0.026, and 0.382±0.022, respectively. Genetic correlations for milk yield between the five states were very low and ranged from 0.070 to 0.364, suggesting the presence of genotype by environment interaction. Differences in genetic and residual variances of milk yield among the states indicate it would be necessary to account for heterogeneous variances in genetic evaluations.

Mixture Hazard Models for Lifetime Data

We propose a general family of mixture hazard models to analyze lifetime data associated with bathtub and multimodal hazard functions. With this model we have a great flexibility for fitting lifetime data. Its version with covariates has the proportional hazard and the accelerated failure time models as special cases. A Bayesian analysis is presented for the model using informative priors, using sampling-based approaches to perform the Bayesian computations. A real example with a medical data illustrates the methodology.

Cure fraction models using mixture and non-mixture models

ABSTRACT We introduce the Weibull distributions in presence of cure fraction, censored data and covariates. Two models are explored in this paper: mixture and non-mixture models. Inferences for the proposed models are obtained under the Bayesian approach, using standard MCMC (Markov Chain Monte Carlo) methods. An illustration of the proposed methodology is given considering a life- time data set.

Bayesian inference for polyhazard models in the presence of covariates

Polyhazard models are a flexible family for fitting lifetime data. Their main advantage over the single hazard models, such as the Weibull and the log-logistic models, is to include a large amount of nonmonotone hazard shapes, as bathtub and multimodal curves. The main goal of this paper is to present a Bayesian inference procedure for the polyhazard models in the presence of covariates, generalizing the Bayesian analysis presented in Berger and Sun (J. Amer. Statist. Assoc. 88 (1993) 1412), Basu et al. (J. Statist. Plan Inference 78 (1999) 255) and Kuo and Yang (Statist. Probab. Lett. 47 (2000) 229). The two most important particular polyhazard models, namely poly-Weibull, poly-log-logistic and a combination of both are studied in detail. The methodology is illustrated in two real medical datasets.

Estimation of Reliability of Multicomponent Stress-Strength for a Kumaraswamy Distribution

Abstract This article deals with the Bayesian and non Bayesian estimation of multicomponent stress–strength reliability by assuming the Kumaraswamy distribution. Both stress and strength are assumed to have a Kumaraswamy distribution with common and known shape parameter. The reliability of such a system is obtained by the methods of maximum likelihood and Bayesian approach and the results are compared using Markov Chain Monte Carlo (MCMC) technique for both small and large samples. Finally, two data sets are analyzed for illustrative purposes.

The inverse power Lindley distribution

ABSTRACT Several probability distributions have been proposed in the literature, especially with the aim of obtaining models that are more flexible relative to the behaviors of the density and hazard rate functions. Recently, two generalizations of the Lindley distribution were proposed in the literature: the power Lindley distribution and the inverse Lindley distribution. In this article, a distribution is obtained from these two generalizations and named as inverse power Lindley distribution. Some properties of this distribution and study of the behavior of maximum likelihood estimators are presented and discussed. It is also applied considering two real datasets and compared with the fits obtained for already-known distributions. When applied, the inverse power Lindley distribution was found to be a good alternative for modeling survival data.

Strategies for the treatment of polymyxin B-resistant Acinetobacter baumannii infections

In this study, the activity of meropenem (MEM), fosfomycin (FOF) and polymyxin B (PMB), alone and in combination, was analysed. In addition, optimisation of the pharmacodynamic index of MEM and FOF against six isolates of OXA-23-producing Acinetobacter baumannii (including three resistant to PMB) that were not clonally related was assessed. Antimicrobial combinations were evaluated by chequerboard analysis and were considered synergistic when the fractional inhibitory concentration index (FICI) was ≤0.5. Pharmacodynamic analyses of the MEM and FOF dosing schemes were performed by Monte Carlo simulation. The target pharmacodynamic index (%ƒT>MIC) for MEM and FOF was ≥40% and ≥70%, respectively, and a probability of target attainment (PTA) ≥0.9 was considered adequate. Among the PMB-resistant isolates, combinations of PMB+MEM and PMB+FOF+MEM showed the highest synergistic activity (FICI ≤0.125); isolates that were previously PMB-resistant were included in the susceptible category using CLSI interpretive criteria. Pharmacodynamic evaluation found that for a FOF minimum inhibitory concentration (MIC) of ≤16μg/mL, treatment both by bolus dosing and prolonged infusion achieved adequate PTA, whilst for MIC=32μg/mL only infusion achieved adequate PTA. For a MEM MIC of 4μg/mL, only the bolus treatment scheme with 1.5g q6h and the infusion schemes with 1.0g q8h, 1.5g q6h and 2.0g q8h achieved PTA ≥0.9. Results of antimicrobial and pharmacodynamic analyses can assist in treating infections caused by multidrug-resistant A. baumannii. However, in vivo clinical studies are essential to evaluate the true role of these compounds, including intravenous antimicrobial FOF therapy.

Bias-corrected maximum likelihood estimation of the parameters of the generalized half-normal distribution

ABSTRACT Cooray and Ananda introduced a two-parameter generalized Half-Normal distribution which is useful for modelling lifetime data, while its maximum likelihood estimators (MLEs) are biased in finite samples. This motivates us to construct nearly unbiased estimators for the unknown parameters of the model. In this paper, we adopt two approaches for bias reduction of the MLEs of the parameters of generalized Half-Normal distribution. The first approach is the analytical methodology suggested by Cox and Snell and the second is based on parametric Bootstrap resampling method. Additionally, the method of moments (MMEs) is used for comparison purposes. The numerical evidence shows that the analytic bias-corrected estimators significantly outperform their bootstrapped-based counterpart for small and moderate samples as well as for MLEs and MMEs. Also, it is apparent from the results that bias- corrected estimates of shape parameter perform better than that of scale parameter. Further, the results show that bias-correction scheme yields nearly unbiased estimates. Finally, six fracture toughness real data sets illustrate the application of our methods.