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This paper presents a novel approach for analyzing bivariate positive data, taking into account a covariate vector and left-censored observations, by introducing a hierarchical Bayesian analysis. The proposed method assumes marginal Weibull distributions and employs either a usual Weibull likelihood or Weibull-Tobit likelihood approaches. A latent variable or frailty is included in the model to capture the possible correlation between the bivariate responses for the same sampling unit. The posterior summaries of interest are obtained through Markov Chain Monte Carlo methods. To demonstrate the effectiveness of the proposed methodology, we apply it to a bivariate data set from stellar astronomy that includes left-censored observations and covariates. Our results indicate that the new bivariate model approach, which incorporates the latent factor to capture the potential dependence between the two responses of interest, produces accurate inference results. We also compare the two models using the different likelihood approaches (Weibull or Weibull-Tobit likelihoods) in the application. Overall, our findings suggest that the proposed hierarchical Bayesian analysis is a promising approach for analyzing bivariate positive data with left-censored observations and covariate information., Competing Interests: No potential conflict of interest was reported by the author(s)., (© 2023 Informa UK Limited, trading as Taylor & Francis Group.)

This study aimed to develop a multiparticulate system based on sodium alginate/gellan gum polymers for morin controlled release using standardized spray-dryer parameters. A 2 4 experimental factorial design was used to standardize spray-dryer parameters. After standardization, three systems with three different proportions of the natural polymers (50:50, 25:75, 75:25; sodium alginate: gellan gum) with and without morin (control) were developed. The systems were characterized according to its morphology and physicochemical properties. Next, the systems were evaluated regarding antibiofilm and antimicrobial activity against Streptococcus mutans. The factorial design indicated the use of the following parameters: i) air flow rate: 1.0 m 3 /min; ii) outlet temperature: 120 °C; iii) natural polymers combination in different proportions; iiii) polymer concentration: 2 %. Scanning electron microscopy showed microparticles with spherical shape and rough surface. The samples released 99.86 % ± 9.36; 85.45 % ± 8.31; 86.87 % ± 3.83 of morin after 480 min. The systems containing morin significantly reduced S. mutans biofilm biomass, microbial viability and acidogenicity when compared to their respective controls. In conclusion, the spray-dryer parameters were standardized to the highest possible yield values and proved to be efficient for morin encapsulation and controlled release. Furthermore, these systems controlled important virulence factors of S. mutans biofilms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Cardiovascular diseases (CVD) are the leading cause of death in the world, and they are considered a serious public health problem in Colombia. The main goal of this study was to analyze CVD mortality spatially and temporarily in the Pacific region of Colombia during the 2002-2015 period, and its association with some municipal socio-economic indicators using spatial statistical analysis techniques. It involved a descriptive-ecological study in the 177 municipalities of the Pacific region that used CVD mortality data, under codes I00-I99 of the International Classification of Diseases (ICD-10), and seven municipal socio-economic indicators. The analysis included the calculation of crude and standardized mortality rates, according to sex, for CVD and its main causes, and modeling of CVD death counts using Bayesian hierarchical models. During the 2002-2015 period, standardized rates of CVD mortality showed a downward trend in men (129.0 to 119.3) and in women (129.0 to 110.0), the main causes of death being ischemic heart diseases, followed by cerebrovascular diseases. In general, the risk of CVD mortality was higher in the less economically and socially privileged municipalities.

Road or urban traffic accidents in Brazil have a large presence in external causes of mortality. The main goal of this study is to discover significant factors in the incidence of accidents on Brazilian highways based on a database with information on each person injured on federal highways in Brazil reported by the Federal Highway Police. Some factors are considered in the study as cause of the accident, type of accident, stage of the day, weather condition, highway type, highway facility, age of the victim, gender of the victim and type of vehicle. From the obtained results of chi-square tests and logistic regression models, it was observed statistical dependence ( p  < 0.05) of the occurrence of injured people with serious injuries and the factors cause of the accident, type of accident, day, highway type and vehicle type. Considering the dead victims, the covariates age, time of day, highway type, highway facility, gender and type of vehicle showed significance ( p  < 0.05). These results are of great interest for authorities to increase road enforcement, improve highway facilities and target the production of vehicles with better safety standards.

In this study, the components of extra-Poisson variability are estimated assuming random effect models under a Bayesian approach. A standard existing methodology to estimate extra-Poisson variability assumes a negative binomial distribution. The obtained results show that using the proposed random effect model it is possible to get more accurate estimates for the extra-Poisson variability components when compared to the use of a negative binomial distribution where it is possible to estimate only one component of extra-Poisson variability. Some illustrative examples are introduced considering real data sets., Competing Interests: No potential conflict of interest was reported by the authors., (© 2020 Informa UK Limited, trading as Taylor & Francis Group.)

In time-to-event studies it is common the presence of a fraction of individuals not expecting to experience the event of interest; these individuals who are immune to the event or cured for the disease during the study are known as long-term survivors. In addition, in many studies it is observed two lifetimes associated to the same individual, and in some cases there exists a dependence structure between them. In these situations, the usual existing lifetime distributions are not appropriate to model data sets with long-term survivors and dependent bivariate lifetimes. In this study, it is proposed a bivariate model based on a Weibull standard distribution with a dependence structure based on fifteen different copula functions. We assumed the Weibull distribution due to its wide use in survival data analysis and its greater flexibility and simplicity, but the presented methods can be adapted to other continuous survival distributions. Three examples, considering real data sets are introduced to illustrate the proposed methodology. A Bayesian approach is assumed to get the inferences for the parameters of the model where the posterior summaries of interest are obtained using Markov Chain Monte Carlo simulation methods and the Openbugs software. For the data analysis considering different real data sets it was assumed fifteen different copula models from which is was possible to find models with satisfactory fit for the bivariate lifetimes in presence of long-term survivors., (© 2020 Published by Elsevier Ltd.)

Generic drugs have been commercialized in numerous countries. Most of these countries approve the commercialization of a generic drug when there is evidence of bioequivalence between the generic drug and the reference drug. Generally, the pharmaceutical industry is responsible for the bioequivalence test under the supervision of a regulatory agency. This procedure is concluded after a statistical data analysis. Several agencies adopt a standard statistical analysis based on procedures that were previously established. In practice, we face situations in which this standard model does not fit to some sets of bioequivalence data. In this study, we propose an evaluation of bioequivalence using univariate and bivariate models based on an extended generalized gamma distribution and a skew-t distribution, under a Bayesian perspective. We introduce a study of the empirical power of hypothesis tests for univariate models, showing advantages in the use of an extended generalized gamma distribution. Three sets of bioequivalence data were analyzed under these new procedures and compared with the standard model proposed by the majority of regulatory agencies. In order to verify that the asymmetrical distributions are usually better fitted for the data, when compared with the standard model, model discrimination methods were used, such as the Deviance Information Criterion (DIC) and quantile-quantile plots. The research concluded that, in general, the use of the extended generalized gamma distribution may be more appropriate to model bioequivalence data in the original scale. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)

Objective: The aim of this study was to analyze social characteristics and stress as correlates of cigarette smoking in adolescence. The main intent was to identify elements that distinguish adolescents who had experimented with smoking and did not progress to regular smoking from those who became current smokers., Methods: Students at 10 high schools in the city of Ribeirão Preto, Brazil, completed a questionnaire based on an instrument employed in a similar large-scale study. The students were classified as never-smokers or experimenters. The experimenters were subcategorized as having become current smokers or nonprogressors. Analyses were performed using adjusted logistic models., Results: A total of 2,014 students (mean age, 16.2 ± 1.1 years; females, 53%) completed the questionnaire. We categorized 1,283 students (63.7%) as never-smokers, 244 (12.1%) as current smokers, and 487 (24.2%) as nonprogressors. We found that experimentation with smoking was associated with being held back a grade in school (OR = 1.80), alcohol intake (low/occasional, OR = 8.92; high/regular, OR = 2.64), illicit drug use (OR = 9.32), having a sibling or cousin who smokes (OR = 1.39), having a friend who smokes (OR = 2.08), and high levels of stress (in females only, OR = 1.32). Factors associated with an increased risk of transitioning from experimenter to current smoker were alcohol intake (low/occasional, OR = 3.28; high/regular, OR = 2.16), illicit drug use (OR = 3.61), and having a friend who smokes (OR = 7.20)., Conclusions: Current smoking was associated with a profile of socioeconomic correlates different from that associated with experimentation only. Our data (showing that current smoking was associated with having a friend who smokes, alcohol intake, and illicit drug use) suggest the need for comprehensive approaches to discourage substance use during adolescence.

2013 marked the 250th anniversary of the presentation of Bayes' theorem by the philosopher Richard Price. Thomas Bayes was a figure little known in his own time, but in the 20th century the theorem that bears his name became widely used in many fields of research. The Bayes theorem is the basis of the so-called Bayesian methods, an approach to statistical inference that allows studies to incorporate prior knowledge about relevant data characteristics into statistical analysis. Nowadays, Bayesian methods are widely used in many different areas such as astronomy, economics, marketing, genetics, bioinformatics and social sciences. This study observed that a number of authors discussed recent advances in techniques and the advantages of Bayesian methods for the analysis of epidemiological data. This article presents an overview of Bayesian methods, their application to epidemiological research and the main areas of epidemiology which should benefit from the use of Bayesian methods in coming years.

Objectives: To characterize a motivational profile of reasons for smoking among teenagers. To investigate the influence of clinical and social elements on observed scores., Methods: High school students who smoked in the past month (n = 226; age, 16.4 ± 10 years; 46.5% male) answered a questionnaire during school time. The instrument included the University of São Paulo Reasons for Smoking Scale (USP-RSS), the Fagerström Test for Nicotine Dependence, and clinical and social information. The USP-RSS scores from 307 healthy adult smokers (67.5% male; age, 37.9 ± 11.2 years) were also used for comparisons., Results: Most of the adolescents (90.2%) exhibited low or very low levels of nicotine addiction (median Fagerström Test for Nicotine Dependence score 0, range 0 to 8). The mean scores of the USP-RSS subscales were as follows: Addiction, 1.9 ± 1.1; Pleasure From Smoking, 3.0 ± 1.3; Tension Reduction, 2.4 ± 1.3; Stimulation, 1.9 ± 0.9; Automatism, 1.3 ± 0.6; Handling, 2.3 ± 1.1; Social Smoking, 1.9 ± 1.0; Weight Control, 1.4 ± 1.0; and Affiliative Attachment, 1.6 ± 0.9. In comparison with adults, teenagers exhibited lower scores for Addiction, Pleasure From Smoking, Tension Reduction, Automatism, Weight Control, and Affiliative Attachment and higher scores for Social Smoking (P < 0.05). Older age, past school failure, illicit drugs use, alcohol abuse, high levels of perceived stress, and the death of at least one parent were associated with high scores for all subscales., Conclusions: The USP-RSS subscales Addiction, Pleasure From Smoking, and Social Smoking were important factors for adolescent smoking. Comparisons with adult smokers stressed the importance of the component of Social Smoking. The identification of distinctive factors that drive teenagers to smoke might help in making decisions dealing with interventions aimed at smoking cessation and control.

This paper presents estimates for the parameters included in long-term mixture and non-mixture lifetime models, applied to analyze survival data when some individuals may never experience the event of interest. We consider the case where the lifetime data have a two-parameters exponentiated exponential distribution. The two-parameter exponentiated exponential or the generalized exponential distribution is a particular member of the exponentiated Weibull distribution introduced by [31]. Classical and Bayesian procedures are used to get point and confidence intervals of the unknown parameters. We consider a general survival model where the scale, shape and cured fraction parameters of the exponentiated exponential distribution depends on covariates., (Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.)

The study was designed to investigate the impact of air pollution on monthly inhalation/nebulization procedures in Ribeirão Preto, São Paulo State, Brazil, from 2004 to 2010. To assess the relationship between the procedures and particulate matter (PM(10)) a Bayesian Poisson regression model was used, including a random factor that captured extra-Poisson variability between counts. Particulate matter was associated with the monthly number of inhalation/nebulization procedures, but the inclusion of covariates (temperature, precipitation, and season of the year) suggests a possible confounding effect. Although other studies have linked particulate matter to an increasing number of visits due to respiratory morbidity, the results of this study suggest that such associations should be interpreted with caution.

Introduction: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using bayesian spatiotemporal methods., Methods: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed., Results: The model proposed here suggests that deforestation rate, the number of inhabitants per km², and the human development index (HDI) are important in the prediction of malaria cases., Conclusions: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the bayesian paradigm is a good strategy for modeling malaria counts.

Teenage pregnancy is a common public health problem worldwide. The objective of this ecological study was to investigate the spatial association between teenage pregnancy rates and socioeconomic characteristics of municipalities in São Paulo State, Southeast Brazil. We used a Bayesian model with a spatial distribution following a conditional autoregressive (CAR) form based on Markov Chain Monte Carlo algorithm. We used data from the Live Birth Information System (SINASC) and the Brazilian Institute of Geography and Statistics (IBGE). Early pregnancy was more frequent in municipalities with lower per capital gross domestic product (GDP), higher poverty rate, smaller population, lower human development index (HDI), and a higher percentage of individuals with State social vulnerability index of 5 or 6 (more vulnerable). The study demonstrates a significant association between teenage pregnancy and socioeconomic indicators.

Sensitivity and specificity are measures that allow us to evaluate the performance of a diagnostic test. In practice, it is common to have situations where a proportion of selected individuals cannot have the real state of the disease verified, since the verification could be an invasive procedure, as occurs with biopsy. This happens, as a special case, in the diagnosis of prostate cancer, or in any other situation related to risks, that is, not practicable, nor ethical, or in situations with high cost. For this case, it is common to use diagnostic tests based only on the information of verified individuals. This procedure can lead to biased results or workup bias. In this paper, we introduce a Bayesian approach to estimate the sensitivity and the specificity for two diagnostic tests considering verified and unverified individuals, a result that generalizes the usual situation based on only one diagnostic test.

In this paper, we introduce a Bayesian analysis for bioequivalence data assuming multivariate pharmacokinetic measures. With the introduction of correlation parameters between the pharmacokinetic measures or between the random effects in the bioequivalence models, we observe a good improvement in the bioequivalence results. These results are of great practical interest since they can yield higher accuracy and reliability for the bioequivalence tests, usually assumed by regulatory offices. An example is introduced to illustrate the proposed methodology by comparing the usual univariate bioequivalence methods with multivariate bioequivalence. We also consider some usual existing discrimination Bayesian methods to choose the best model to be used in bioequivalence studies.

Objective: To develop a statistical model based on Bayesian methods to estimate the risk of tuberculosis infection in studies including individuals lost to follow-up, and to compare it with a classic deterministic model., Methods: The proposed stochastic model is based on a Gibbs sampling algorithm that uses information of lost to follow-up at the end of a longitudinal study. For simulating the unknown number of reactors at the end of the study and lost to follow-up, but not reactors at time 0, a latent variable was introduced in the new model. An exercise of application of both models in the comparison of the estimates of interest was presented., Results: The point estimates obtained from both models are near identical; however, the Bayesian model allowed the estimation of credible intervals as measures of precision of the estimated parameters., Conclusions: The Bayesian model can be valuable in longitudinal studies with low adherence to follow-up.

In this paper we develop a Bayesian analysis to estimate the disease prevalence, the sensitivity and specificity of three cervical cancer screening tests (cervical cytology, visual inspection with acetic acid and Hybrid Capture II) in the presence of a covariate and in the absence of a gold standard. We use Metropolis-Hastings algorithm to obtain the posterior summaries of interest. The estimated prevalence of cervical lesions was 6.4% (a 95% credible interval [95% CI] was 3.9, 9.3). The sensitivity of cervical cytology (with a result of >or= ASC-US) was 53.6% (95% CI: 42.1, 65.0) compared with 52.9% (95% CI: 43.5, 62.5) for visual inspection with acetic acid and 90.3% (95% CI: 76.2, 98.7) for Hybrid Capture II (with result of >1 relative light units). The specificity of cervical cytology was 97.0% (95% CI: 95.5, 98.4) and the specificities for visual inspection with acetic acid and Hybrid Capture II were 93.0% (95% CI: 91.0, 94.7) and 88.7% (95% CI: 85.9, 91.4), respectively. The Bayesian model with covariates suggests that the sensitivity and the specificity of the visual inspection with acetic acid tend to increase as the age of the women increases. The Bayesian method proposed here is an useful alternative to estimate measures of performance of diagnostic tests in the presence of covariates and when a gold standard is not available. An advantage of the method is the fact that the number of parameters to be estimated is not limited by the number of observations, as it happens with several frequentist approaches. However, it is important to point out that the Bayesian analysis requires informative priors in order for the parameters to be identifiable. The method can be easily extended for the analysis of other medical data sets.

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 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.

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.