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Cefepime and piperacillin/tazobactam are antimicrobials recommended by IDSA/ATS guidelines for the empirical management of patients admitted to the intensive care unit (ICU) with community-acquired pneumonia (CAP). Concerns have been raised about which should be used in clinical practice. This study aims to compare the effect of cefepime and piperacillin/tazobactam in critically ill CAP patients through a targeted maximum likelihood estimation (TMLE). A total of 2026 ICU-admitted patients with CAP were included. Among them, (47%) presented respiratory failure, and (27%) developed septic shock. A total of (68%) received cefepime and (32%) piperacillin/tazobactam-based treatment. After running the TMLE, we found that cefepime and piperacillin/tazobactam-based treatments have comparable 28-day, hospital, and ICU mortality. Additionally, age, PTT, serum potassium and temperature were associated with preferring cefepime over piperacillin/tazobactam (OR 1.14 95% CI [1.01-1.27], p = 0.03), (OR 1.14 95% CI [1.03-1.26], p = 0.009), (OR 1.1 95% CI [1.01-1.22], p = 0.039) and (OR 1.13 95% CI [1.03-1.24], p = 0.014)]. Our study found a similar mortality rate among ICU-admitted CAP patients treated with cefepime and piperacillin/tazobactam. Clinicians may consider factors such as availability and safety profiles when making treatment decisions.

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We introduce the bivariate unit-log-symmetric model based on the bivariate log-symmetric distribution (BLS) defined in Vila et al. [25] as a flexible family of bivariate distributions over the unit square. We then study its mathematical properties such as stochastic representations, quantiles, conditional distributions, independence of the marginal distributions and marginal moments. Maximum likelihood estimation method is discussed and examined through Monte Carlo simulation. Finally, the proposed model is used to analyze some soccer data sets.

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This paper aims to introduce a novel family of probability distributions by the well-known method of the T-X family of distributions. The proposed family is called a "Novel Generalized Exponent Power X Family" of distributions. A three-parameters special sub-model of the proposed method is derived and named a "Novel Generalized Exponent Power Weibull" distribution (NGEP-Wei for short). For the proposed family, some statistical properties are derived including the hazard rate function, moments, moment generating function, order statistics, residual life, and reverse residual life. The well-known method of estimation, the maximum likelihood estimation method is used for estimating the model parameters. Besides, a comprehensive Monte Carlo simulation study is conducted to assess the efficacy of this estimation method. Finally, the model selection criterion such as Akaike information criterion (AINC), the correct information criterion (CINC), the Bayesian information criterion (BINC), the Hannan-Quinn information criterion (HQINC), the Cramer-von-Misses (CRMI), and the ANDA (Anderson-Darling) are used for comparison purpose. The comparison of the NGEP-Wei with other rival distributions is made by Two COVID-19 data sets. In terms of performance, we show that the proposed method outperforms the other competing methods included in this study.

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BACKGROUND: Cross-sectional studies are useful for the estimation of prevalence of a particular event with concerns in specific populations, as in the case of diseases or other public health interests. Most of these studies have been carried out with binary binomial logistic regression model which estimates OR values that could be overestimated due to the adjustment of the model. Thus, the selection of the best multivariate model for cross-sectional studies is a priority to control the overestimation of the associations. METHODS: We compared the precision of the estimates of the prevalence ratio (PR) of the negative Log-binomial model (NLB) with Mantel-Haenszel (MH) and the regression models Cox, Log-Poisson, Log-binomial, and the OR of the binary logistic regression in population-based cross-sectional studies. The prevalence from a previous cross-sectional study carried out in Colombia about the association of mental health disorders with the consumption of psychoactive substances (e.g., cocaine, marijuana, cigarette, alcohol and risk of consumption of psychoactive substances) were used. The precision of the point estimates of the PR was evaluated for the NLB model with robust variance estimates, controlled with confounding variables, and confidence interval of 95%. RESULTS: The NLB model adjusted with robust variance showed accuracy in the measurements of crude PRs, standard errors of estimate and its corresponding confidence intervals (95%CI) as well as a high precision of the PR estimate and standard errors of estimate after the adjustment of the model by grouped age compared with the MH PR estimate. Obtained PRs and 95%CI entre NLB y MH were: cocaine consumption (2.931,IC95%: 0.723-11.889 vs. 2.913, IC95%: 0.786-12.845), marijuana consumption (3.444, IC95%: 1.856-6.391 vs. 3.407, IC95%: 1.848, 6.281), cigarette smoking (2.175,IC95%: 1.493, 3.167 vs. 2.209, IC95%: 1.518-3.214), alcohol consumption (1.243,IC95%: 1.158-1.334 vs. 1.241, IC95%: 1.157-1.332), and risk of consumption of psychoactive substances (1.086, IC95%: 1.047-1.127 vs. 1.086, IC95%: 1.047, 1.126). The NLB model adjusted with robust variance showed mayor precision when increasing the prevalence, then the other models with robust variance with respect to MH. CONCLUSIONS: The NLB model with robust variance was shown as a powerful strategy for the estimation of PRs for cross-sectional population-based studies, as high precision levels were identified for point estimators, standard errors of estimate and its corresponding confidence intervals, after the adjustment of confounding variables. In addition, it does not represent convergence issues for high prevalence cases (as it occur with the Log-binomial model) and could be considered in cases of overdispersion and with greater precision and goodness of fit than the other models with robust variance, as it was shown with the data set of the cross-sectional study used in here.

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This article proposes a system for Content-Based Image Retrieval (CBIR) using stochastic distance for Synthetic-Aperture Radar (SAR) images. The methodology consists of three essential steps for image retrieval. First, it estimates the roughness (α^) and scale (γ^) parameters of the GI0 distribution that models SAR data in intensity. The parameters of the model were estimated using the Maximum Likelihood Estimation and the fast approach of the Log-Cumulants method. Second, using the triangular distance, CBIR-SAR evaluates the similarity between a query image and images in the database. The stochastic distance can identify the most similar regions according to the image features, which are the estimated parameters of the data model. Third, the performance of our proposal was evaluated by applying the Mean Average Precision (MAP) measure and considering clippings from three radar sensors, i.e., UAVSAR, OrbiSaR-2, and ALOS PALSAR. The CBIR-SAR results for synthetic images achieved the highest MAP value, retrieving extremely heterogeneous regions. Regarding the real SAR images, CBIR-SAR achieved MAP values above 0.833 for all polarization channels for image samples of forest (UAVSAR) and urban areas (ORBISAR). Our results confirmed that the proposed method is sensitive to the degree of texture, and hence, it relies on good estimates. They are inputs to the stochastic distance for effective image retrieval.

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BACKGROUND: Accurate determination of the effective reproduction number (Rt) is a very important strategy in the epidemiology of contagious diseases, including coronavirus disease 2019 (COVID-19). This study compares different methods of estimating the Rt of susceptible population to identify the most accurate method for estimating Rt. STUDY DESIGN: A secondary study. METHODS: The value of Rt was estimated using attack rate (AR), exponential growth (EG), maximum likelihood (ML), time-dependent (TD), and sequential Bayesian (SB) methods, for Iran, the United States, the United Kingdom, India, and Brazil from June to October 2021. In order to accurately compare these methods, a simulation study was designed using forty scenarios. RESULTS: The lowest mean square error (MSE) was observed for TD and ML methods, with 15 and 12 cases, respectively. Therefore, considering the estimated values of Rt based on the TD method, it was found that Rt values in the United Kingdom (1.33; 95% CI: 1.14-1.52) and the United States (1.25; 95% CI: 1.12-1.38) substantially have been more than those in other countries, such as Iran (1.07; 95% CI: 0.95-1.19), India (0.99; 95% CI: 0.89-1.08), and Brazil (0.98; 95% CI: 0.84-1.14) from June to October 2021. CONCLUSION: The important result of this study is that TD and ML methods lead to a more accurate estimation of Rt of population than other methods. Therefore, in order to monitor and determine the epidemic situation and have a more accurate prediction of the incidence rate, as well as control COVID-19 and similar diseases, the use of these two methods is suggested to more accurately estimate Rt.

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Natural fibers used as reinforcements or fillers for materials development greatly affect properties and performance of end-use applications. As a consequence of conditioning processes such as grinding and sieving, average fiber length varies significantly. It is thus necessary to estimate the length as statistical data distribution rather than a single mean value. This approach implies length measurement of a significant number of fibers; however, a very high number of data points requires not only long-time frames but also significative amount of work. To address these issues, this article details a facile methodology to measure the length of a large number of natural fibers of oil palm empty fruit bunch (OPEFB) together with a statistical analysis to verify the correspondence between theoretical distributions and experimental data. Moreover, further information related to spectrophotometric, physico-chemical, mechanical, thermal, and morphological characteristics of OPEFB fibers coming from oil palm cultivation in Ecuador are presented. The data will contribute to comprehensively and rigorously describe the overall effects of natural fiber lengths on material properties.

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Dengue fever is a tropical disease transmitted mainly by the female Aedes aegypti mosquito that affects millions of people every year. As there is still no safe and effective vaccine, currently the best way to prevent the disease is to control the proliferation of the transmitting mosquito. Since the proliferation and life cycle of the mosquito depend on environmental variables such as temperature and water availability, among others, statistical models are needed to understand the existing relationships between environmental variables and the recorded number of dengue cases and predict the number of cases for some future time interval. This prediction is of paramount importance for the establishment of control policies. In general, dengue-fever datasets contain the number of cases recorded periodically (in days, weeks, months or years). Since many dengue-fever datasets tend to be of the overdispersed, long-tail type, some common models like the Poisson regression model or negative binomial regression model are not adequate to model it. For this reason, in this paper we propose modeling a dengue-fever dataset by using a Poisson-inverse-Gaussian regression model. The main advantage of this model is that it adequately models overdispersed long-tailed data because it has a wider skewness range than the negative binomial distribution. We illustrate the application of this model in a real dataset and compare its performance to that of a negative binomial regression model.

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The sample selection bias problem occurs when the outcome of interest is only observed according to some selection rule, where there is a dependence structure between the outcome and the selection rule. In a pioneering work, J. Heckman proposed a sample selection model based on a bivariate normal distribution for dealing with this problem. Due to the non-robustness of the normal distribution, many alternatives have been introduced in the literature by assuming extensions of the normal distribution like the Student-t and skew-normal models. One common limitation of the existent sample selection models is that they require a transformation of the outcome of interest, which is common R + -valued, such as income and wage. With this, data are analyzed on a non-original scale which complicates the interpretation of the parameters. In this paper, we propose a sample selection model based on the bivariate Birnbaum-Saunders distribution, which has the same number of parameters that the classical Heckman model. Further, our associated outcome equation is R + -valued. We discuss estimation by maximum likelihood and present some Monte Carlo simulation studies. An empirical application to the ambulatory expenditures data from the 2001 Medical Expenditure Panel Survey is presented.

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In this paper, two new general families of distributions supported on the unit interval are introduced. The proposed families include several known models as special cases and define at least twenty (each one) new special models. Since the list of well-being indicators may include several double bounded random variables, the applicability for modeling those is the major practical motivation for introducing the distributions on those families. We propose a parametrization of the new families in terms of the median and develop a shiny application to provide interactive density shape illustrations for some special cases. Various properties of the introduced families are studied. Some special models in the new families are discussed. In particular, the complementary unit Weibull distribution is studied in some detail. The method of maximum likelihood for estimating the model parameters is discussed. An extensive Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples. Applications to the literacy rate in Brazilian and Colombian municipalities illustrate the usefulness of the two new families for modeling well-being indicators.

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In this work, we propose a spatio-temporal Markovian-like model for ordinal observations to predict in time the spread of disease in a discrete rectangular grid of plants. This model is constructed from a logistic distribution and some simple assumptions that reflect the conditions present in a series of studies carried out to understand the dissemination of a particular infection in plants. After constructing the model, we establish conditions for the existence and uniqueness of the maximum likelihood estimator (MLE) of the model parameters. In addition, we show that, under further restrictions based on Partially Ordered Markov Models (POMMs), the MLE of the model is consistent and normally asymptotic. We then employ the MLE's asymptotic normality to propose methods for testing spatio-temporal and spatial dependencies. The model is estimated from the real data on plants that inspired the model, and we used its results to construct prediction maps to better understand the transmission of plant illness in time and space.

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Low- and middle-income countries (LMICs) are experiencing rapid aging, a growing dementia burden, and relatively high rates of out-migration among working-age adults. Family member migration status may be a unique societal determinant of cognitive aging in LMIC settings. We aimed to evaluate the association between adult child US migration status and change in cognitive performance scores using data from the Mexican Health and Aging Study, a population-based, national-level cohort study of Mexico adults aged ≥50 years at baseline (2001), with 2-, 12-, and 14-year follow-up waves (2003, 2012, and 2015). Cognitive performance assessments were completed by 5,972 and 4,939 respondents at 11 years and 14 years of follow-up, respectively. For women, having an adult child in the United States was associated with steeper decline in verbal memory scores (e.g., for 9-year change in immediate verbal recall z score, marginal risk difference (RD) = -0.09 (95% confidence interval (CI): -0.16, -0.03); for delayed verbal recall z score, RD = -0.10 (95% CI: -0.17, -0.03)) and overall cognitive performance (for overall cognitive performance z score, RD = -0.04, 95% CI: -0.07, -0.00). There were mostly null associations for men. To our knowledge, this is the first study to have evaluated the association between family member migration status and cognitive decline; future work should be extended to other LMICs facing population aging.

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Frailty models are generally used to model heterogeneity between the individuals. The distribution of the frailty variable is often assumed to be continuous. However, there are situations where a discretely-distributed frailty may be appropriate. In this paper, we propose extending the proportional hazards frailty models to allow a discrete distribution for the frailty variable. Having zero frailty can be interpreted as being immune or cured (long-term survivors). Thus, we develop a new survival model induced by discrete frailty with zero-inflated power series distribution, which can account for overdispersion. A numerical study is carried out under the scenario that the baseline distribution follows an exponential distribution, however this assumption can be easily relaxed and some other distributions can be considered. Moreover, this proposal allows for a more realistic description of the non-risk individuals, since individuals cured due to intrinsic factors (immune) are modeled by a deterministic fraction of zero-risk while those cured due to an intervention are modeled by a random fraction. Inference is developed by the maximum likelihood method for the estimation of the model parameters. A simulation study is performed in order to evaluate the performance of the proposed inferential method. Finally, the proposed model is applied to a data set on malignant cutaneous melanoma to illustrate the methodology.

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The restricted maximum likelihood method was used to assess performance following the introduction of improved varieties of chickpea and mungbean (an important source of plant protein in Afghanistan) as compared to local varieties using 242 farmer participatory demonstrations laid out in eight districts in Baghlan, Balkh and Uruzgan provinces in Afghanistan from 2009 to 2012. The impact of the varieties introduced on the enhancement of security of food and nutrition of farmers adopting such technologies was also assessed. Taking an average over the study period, chickpea improved varieties (Madad and Sehat) recorded 56 and 72 % more yield over the local ones, respectively, while in case of mungbean varieties, Mai 2008 and Maash 2008 recorded 22 and 30 % more yield over local ones respectively. Though there is a significant yield difference between the improved and the local varieties of both crops, the difference between the improved varieties of chickpea was not significant while it was significant in the case of mungbean. The study revealed a non-zero variance component for variety type [improved vs. local] × year within district interaction for the yield of chickpea while none of the interactions in mungbean had a positive variance component. Risk analysis showed that at a chosen probability level of 90 %, the improved varieties yielded more than local varieties in both crops (> 1.0 t ha1). Thus, the study highlighted the scope for enhancing the security of both food and nutrition in Afghanistan through improved productivity of pulse crops.(AU)

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The restricted maximum likelihood method was used to assess performance following the introduction of improved varieties of chickpea and mungbean (an important source of plant protein in Afghanistan) as compared to local varieties using 242 farmer participatory demonstrations laid out in eight districts in Baghlan, Balkh and Uruzgan provinces in Afghanistan from 2009 to 2012. The impact of the varieties introduced on the enhancement of security of food and nutrition of farmers adopting such technologies was also assessed. Taking an average over the study period, chickpea improved varieties (Madad and Sehat) recorded 56 and 72 % more yield over the local ones, respectively, while in case of mungbean varieties, Mai 2008 and Maash 2008 recorded 22 and 30 % more yield over local ones respectively. Though there is a significant yield difference between the improved and the local varieties of both crops, the difference between the improved varieties of chickpea was not significant while it was significant in the case of mungbean. The study revealed a non-zero variance component for variety type [improved vs. local] × year within district interaction for the yield of chickpea while none of the interactions in mungbean had a positive variance component. Risk analysis showed that at a chosen probability level of 90 %, the improved varieties yielded more than local varieties in both crops (> 1.0 t ha1). Thus, the study highlighted the scope for enhancing the security of both food and nutrition in Afghanistan through improved productivity of pulse crops.

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The cusp catastrophe model is an innovative approach for investigating a phenomenon that consists of both continuous and discrete changes in one modeling framework. However, its application to empirical health and behavior data has been hindered by the complexity in data-model fit. In this study, we reported our work in the development of a new modeling method-cusp catastrophe regression (RegCusp in short) by casting the cusp catastrophe into a statistical regression. With the RegCusp approach, unbiased model parameters can be estimated with the maximum likelihood estimation method. To validate the RegCusp method, a series of simulations were conducted to demonstrate the unbiasedness of parameter estimation. Since the estimated residual variance with the Fisher information matrix method was over-dispersed, a bootstrap re-sampling procedure was developed and used as a remedy. We also demonstrate the practical applicability of the RegCusp with empirical data from an NIH-funded project to evaluate an HIV prevention intervention program to educate adolescents in the Bahamas for condom use. Study findings indicated that the model parameters estimated with RegCusp were practically more meaningful than those estimated with comparable methods, especially the estimated cusp point.

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Abstract We propose a new four-parameter lifetime model, called the extended log-logistic distribution, to generalize the two-parameter log-logistic model. The new model is quite flexible to analyze positive data. We provide some mathematical properties including explicit expressions for the ordinary and incomplete moments, probability weighted moments, mean deviations, quantile function and entropy measure. The estimation of the model parameters is performed by maximum likelihood using the BFGS algorithm. The flexibility of the new model is illustrated by means of an application to a real data set. We hope that the new distribution will serve as an alternative model to other useful distributions for modeling positive real data in many areas.

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Recently, we demonstrated the existence of nonextensive behavior in neuromuscular transmission (da Silva et al. in Phys Rev E 84:041925, 2011). In this letter, we first obtain a maximum-likelihood q-estimator to calculate the scale factor ([Formula: see text]) and the q-index of q-Gaussian distributions. Next, we use the indexes to analyze spontaneous miniature end plate potentials in electrophysiological recordings from neuromuscular junctions. These calculations were performed assuming both normal and high extracellular potassium concentrations [Formula: see text]. This protocol was used to test the validity of Tsallis statistics under electrophysiological conditions closely resembling physiological stimuli. The analysis shows that q-indexes are distinct depending on the extracellular potassium concentration. Our letter provides a general way to obtain the best estimate of parameters from a q-Gaussian distribution function. It also expands the validity of Tsallis statistics in realistic physiological stimulus conditions. In addition, we discuss the physical and physiological implications of these findings.

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La mayoría de los datos en ciencias sociales y educación presentan valores perdidos debido al abandono del estudio o la ausencia de respuesta. Los métodos para el manejo de datos perdidos han mejorado dramáticamente en los últimos años, y los programas computacionales ofrecen en la actualidad una variedad de opciones sofisticadas. A pesar de la amplia disponibilidad de métodos considerablemente justificados, muchos investigadores e investigadoras siguen confiando en técnicas viejas de imputación que pueden crear análisis sesgados. Este artículo presenta una introducción conceptual a los patrones de datos perdidos. Seguidamente, se introduce el manejo de datos perdidos y el análisis de los mismos con base en los mecanismos modernos del método de máxima verosimilitud con información completa (FIML, siglas en inglés) y la imputación múltiple (IM). Asimismo, se incluye una introducción a los diseños de datos perdidos así como nuevas herramientas computacionales tales como la función Quark y el paquete semTools. Se espera que este artículo incentive el uso de métodos modernos para el análisis de los datos perdidos.

Most of the social and educational data have missing observations due to either attrition or nonresponse. Missing data methodology has improved dramatically in recent years, and popular computer programs as well as software now offer a variety of sophisticated options. Despite the widespread availability of theoretically justified methods, many researchers still rely on old imputation techniques that can create biased analysis. This article provides conceptual introductions to the patterns of missing data. In line with that, this article introduces how to handle and analyze the missing information based on modern mechanisms of full-information maximum likelihood (FIML) and multiple imputation (MI). An introduction about planned missing designs is also included and new computational tools like Quark function, and semTools package are also mentioned. The authors hope that this paper encourages researchers to implement modern methods for analyzing missing data.

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In this paper, we introduce a new model for recurrent event data characterized by a baseline rate function fully parametric, which is based on the exponential-Poisson distribution. The model arises from a latent competing risk scenario, in the sense that there is no information about which cause was responsible for the event occurrence. Then, the time of each recurrence is given by the minimum lifetime value among all latent causes. The new model has a particular case, which is the classical homogeneous Poisson process. The properties of the proposed model are discussed, including its hazard rate function, survival function, and ordinary moments. The inferential procedure is based on the maximum likelihood approach. We consider an important issue of model selection between the proposed model and its particular case by the likelihood ratio test and score test. Goodness of fit of the recurrent event models is assessed using Cox-Snell residuals. A simulation study evaluates the performance of the estimation procedure in the presence of a small and moderate sample sizes. Applications on two real data sets are provided to illustrate the proposed methodology. One of them, first analyzed by our team of researchers, considers the data concerning the recurrence of malaria, which is an infectious disease caused by a protozoan parasite that infects red blood cells.

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