Percentiles de circunferencia de cintura en escolares de Bogotá, Colombia Estudio FUPRECOL

Objetivo: Determinar la distribución por percentiles de la circunferencia de cintura en una población escolar de Bogotá, Colombia, pertenecientes al estudio FUPRECOL. Métodos: Estudio transversal, realizado en 3.005 niños y 2.916 adolescentes de entre 9 y 17,9 años de edad, de Bogotá, Colombia. Se tomaron medidas de peso, talla, circunferencia de cintura, circunferencia de cadera y estado de maduración sexual por auto-reporte. Se calcularon los percentiles (P3, P10, P25, P50, P75, P90 y P97) y curvas centiles según sexo y edad. Se realizó una comparación entre los valores de la circunferencia de cintura observados con estándares internacionales. Resultados: De la población general (n=5.921), el 57,0% eran chicas (promedio de edad 12,7±2,3 años). En la mayoría de los grupos etáreos la circunferencia de cintura de las chicas fue inferior a la de los chicos. El aumento entre el P50-P97 de la circunferencia de cintura , por edad, fue mínimo de 15,7 cm en chicos de 9-9.9 años y de 16,0 cm en las chicas de 11-11.9 años. Al comparar los resultados de este estudio, por grupos de edad y sexo, con trabajos internacionales de niños y adolescentes, el P50 fue inferior al reportado en Perú e Inglaterra a excepción de los trabajos de la India, Venezuela (Mérida), Estados Unidos y España. Conclusiones: Se presentan percentiles de la circunferencia de cintura según edad y sexo que podrán ser usados de referencia en la evaluación del estado nutricional y en la predicción del riesgo cardiovascular desde edades tempranas.

Meta-analysis: a unifying meta-likelihood approach framing unobserved heterogeneity, study covariates, publication bias, and study quality

OBJECTIVES: This contribution provides a unifying concept for meta-analysis integrating the handling of unobserved heterogeneity, study covariates, publication bias and study quality. It is important to consider these issues simultaneously to avoid the occurrence of artifacts, and a method for doing so is suggested here. METHODS: The approach is based upon the meta-likelihood in combination with a general linear nonparametric mixed model, which lays the ground for all inferential conclusions suggested here. RESULTS: The concept is illustrated at hand of a meta-analysis investigating the relationship of hormone replacement therapy and breast cancer. The phenomenon of interest has been investigated in many studies for a considerable time and different results were reported. In 1992 a meta-analysis by Sillero-Arenas et al. concluded a small, but significant overall effect of 1.06 on the relative risk scale. Using the meta-likelihood approach it is demonstrated here that this meta-analysis is due to considerable unobserved heterogeneity. Furthermore, it is shown that new methods are available to model this heterogeneity successfully. It is argued further to include available study covariates to explain this heterogeneity in the meta-analysis at hand. CONCLUSIONS: The topic of HRT and breast cancer has again very recently become an issue of public debate, when results of a large trial investigating the health effects of hormone replacement therapy were published indicating an increased risk for breast cancer (risk ratio of 1.26). Using an adequate regression model in the previously published meta-analysis an adjusted estimate of effect of 1.14 can be given which is considerably higher than the one published in the meta-analysis of Sillero-Arenas et al. In summary, it is hoped that the method suggested here contributes further to a good meta-analytic practice in public health and clinical disciplines.

Use of the ratio plot in capture-recapture estimation

Statistical graphics are a fundamental, yet often overlooked, set of components in the repertoire of data analytic tools. Graphs are quick and efficient, yet simple instruments of preliminary exploration of a dataset to understand its structure and to provide insight into influential aspects of inference such as departures from assumptions and latent patterns. In this paper, we present and assess a graphical device for choosing a method for estimating population size in capture-recapture studies of closed populations. The basic concept is derived from a homogeneous Poisson distribution where the ratios of neighboring Poisson probabilities multiplied by the value of the larger neighbor count are constant. This property extends to the zero-truncated Poisson distribution which is of fundamental importance in capture–recapture studies. In practice however, this distributional property is often violated. The graphical device developed here, the ratio plot, can be used for assessing specific departures from a Poisson distribution. For example, simple contaminations of an otherwise homogeneous Poisson model can be easily detected and a robust estimator for the population size can be suggested. Several robust estimators are developed and a simulation study is provided to give some guidance on which should be used in practice. More systematic departures can also easily be detected using the ratio plot. In this paper, the focus is on Gamma mixtures of the Poisson distribution which leads to a linear pattern (called structured heterogeneity) in the ratio plot. More generally, the paper shows that the ratio plot is monotone for arbitrary mixtures of power series densities.

Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation

Many modern statistical applications involve inference for complex stochastic models, where it is easy to simulate from the models, but impossible to calculate likelihoods. Approximate Bayesian computation (ABC) is a method of inference for such models. It replaces calculation of the likelihood by a step which involves simulating artificial data for different parameter values, and comparing summary statistics of the simulated data with summary statistics of the observed data. Here we show how to construct appropriate summary statistics for ABC in a semi-automatic manner. We aim for summary statistics which will enable inference about certain parameters of interest to be as accurate as possible. Theoretical results show that optimal summary statistics are the posterior means of the parameters. Although these cannot be calculated analytically, we use an extra stage of simulation to estimate how the posterior means vary as a function of the data; and we then use these estimates of our summary statistics within ABC. Empirical results show that our approach is a robust method for choosing summary statistics that can result in substantially more accurate ABC analyses than the ad hoc choices of summary statistics that have been proposed in the literature. We also demonstrate advantages over two alternative methods of simulation-based inference.

Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.

A matrix formula for the skewness of maximum likelihood estimators

We give a general matrix formula for computing the second-order skewness of maximum likelihood estimators. The formula was firstly presented in a tensorial version by Bowman and Shenton (1998). Our matrix formulation has numerical advantages, since it requires only simple operations on matrices and vectors. We apply the second-order skewness formula to a normal model with a generalized parametrization and to an ARMA model. (c) 2010 Elsevier B.V. All rights reserved.

Improved maximum-likelihood estimation in a regression model with general parametrization

We analyse the finite-sample behaviour of two second-order bias-corrected alternatives to the maximum-likelihood estimator of the parameters in a multivariate normal regression model with general parametrization proposed by Patriota and Lemonte [A. G. Patriota and A. J. Lemonte, Bias correction in a multivariate regression model with genereal parameterization, Stat. Prob. Lett. 79 (2009), pp. 1655-1662]. The two finite-sample corrections we consider are the conventional second-order bias-corrected estimator and the bootstrap bias correction. We present the numerical results comparing the performance of these estimators. Our results reveal that analytical bias correction outperforms numerical bias corrections obtained from bootstrapping schemes.

Likelihood estimate of treatment effects under selection bias

We consider methods for estimating causal effects of treatment in the situation where the individuals in the treatment and the control group are self selected, i.e., the selection mechanism is not randomized. In this case, simple comparison of treated and control outcomes will not generally yield valid estimates of casual effects. The propensity score method is frequently used for the evaluation of treatment effect. However, this method is based onsome strong assumptions, which are not directly testable. In this paper, we present an alternative modeling approachto draw causal inference by using share random-effect model and the computational algorithm to draw likelihood based inference with such a model. With small numerical studies and a real data analysis, we show that our approach gives not only more efficient estimates but it is also less sensitive to model misspecifications, which we consider, than the existing methods.

Likelihood prediction for generalized linear mixed models under covariate uncertainty

This paper presents the techniques of likelihood prediction for the generalized linear mixed models. Methods of likelihood prediction is explained through a series of examples; from a classical one to more complicated ones. The examples show, in simple cases, that the likelihood prediction (LP) coincides with already known best frequentist practice such as the best linear unbiased predictor. The paper outlines a way to deal with the covariate uncertainty while producing predictive inference. Using a Poisson error-in-variable generalized linear model, it has been shown that in complicated cases LP produces better results than already know methods.

Acurácia dos indicadores clínicos do diagnóstico de enfermagem volume de líquidos excessivo em pacientes submetidos à hemodiálise

Trasnversal study, with the objective of evaluating the accuracy of clinical indicators of nursing diagnosis excessive fluid volume in patients undergoing hemodialysis. The study occurred in two stages, the first consisted of the evaluation of the diagnostic indicators in study; and the second, the diagnostic inference conducted by nurse diagnosticians. The first stage occurred from december 2012 to april 2013, in a University Hospital and a Hemodialysis Clinic in Northeastern of Brazil, with a sample of 100 chronic renal failure patients on hemodialysis. The data were selected through an interview form and a physical examination, organized into spreadsheets and analyzed as to the presence or absence of the indicators of diagnosis excessive fluid volume. In the second step, the spreadsheets were sent to three nurses diagnosticians, who judged the presence or absence of diagnosis in the clientele searched. This step was conducted from july to september 2013. For analysis of the data, we used descriptive and inferential statistics. In the descriptive analysis, we used measures of central tendency and dispersion. In inferential analysis, we used the tests Chi- square, Fisher and prevalence ratios. The accuracy of the clinical indicators pertaining to the diagnosis were measured as to the specificity, sensitivity, predictive values, likelihood ratios and Diagnostic Odds Ratio. Also developed a logistic regression. The results were organized in tables and discussed with literature. This study was approved by the Ethics Committee in Research of the Federal University of Rio Grande do Norte, with Presentation Certificate for Ethics Appreciation nº 08696212.7.0000.5537. The results revealed that the diagnosis studied was present in 82% of patients. The characteristics with prevalence above 50 % that stood out were: azotemia, decreased hematocrit, electrolyte imbalance, intake exceeds output, anxiety, edema, decreased hemoglobin, oliguria and blood pressure changes. Eight defining characteristics were presented statistically significant association with the nursing diagnosis investigated: pulmonary congestion, intake exceeds output, electrolytes imbalance, jugular vein distension, edema, weight gain over short period of time, agitation and adventitious breath sounds. Among these, the 10 characteristics which showed higher prevalence ratios were: edema and weight gain over short period of time. The features with the highest sensitivity were edema, electrolytes imbalance and intake exceeds output and the standing out with greater specificity were: anasarca, weight gain over short period of time, change in respiratory pattern, adventitious breath sounds, pulmonary congestion, agitation and jugular vein distension. The indicators jugular vein distension, electrolytes imbalance, intake exceeds output, increased central venous pressure and edema, together, were identified in the logistic regression model as the most significant predictors. It is concluded that the identification of accurate clinical indicators allow a good prediction of the nursing diagnosis of excessive fluid volume in patients undergoing hemodialysis in order to assist the nurse in the inference process, which will contribute to the success of patient care. In addition, nurses will consider for diagnostic inference not only his clinical experience, but also scientific evidence of the occurrence of excessive fluid volume, contributing to the control of volemia in these patients

Likelihood approximations and discrete models for tied survival data

Ties among event times are often recorded in survival studies. For example, in a two week laboratory study where event times are measured in days, ties are very likely to occur. The proportional hazards model might be used in this setting using an approximated partial likelihood function. This approximation works well when the number of ties is small. on the other hand, discrete regression models are suggested when the data are heavily tied. However, in many situations it is not clear which approach should be used in practice. In this work, empirical guidelines based on Monte Carlo simulations are provided. These recommendations are based on a measure of the amount of tied data present and the mean square error. An example illustrates the proposed criterion.

A constraint on the x dependence of the light antiquarks ratio

We perform a careful study on the effect of the Pauli blocking to the light antiquark structure of the proton sea. We develop the formal expressions for the antiquark distributions, highlighting the role played by quark statistics and the vacuum structure. Ratios involving the antiquarks are calculated. In particular, it is found that Delta(d) over bar (x)/Delta(u) over bar (x) should be negative and x independent. (C) 2002 Elsevier B.V. B.V. All rights reserved.

Second-order statistics for diversity-combining, techniques in Nakagami-fading channels

Exact and closed-form expressions for the level crossing rate and average fade duration are presented for the M branch pure selection combining (PSC), equal gain combining (EGC), and maximal ratio combining (MRC) techniques, assuming independent branches in a Nakagami environment. The analytical results are thoroughly validated by reducing the general case to some special cases, for which the solutions are known, and by means of simulation for the more general case. The model developed here is general and can be easily applied to other fading statistics (e.g., Rice).

Assessment and statistics of Brazilian hydroelectric power plants: Dam areas versus installed and firm power

The Brazilian relief, predominantly composed by small mountains and plateaus, contributed to formation of rivers with high amount of falls. With exception to North-eastern Brazil, the climate of this country are rainy, which contributes to maintain water flows high. These elements are essential to a high hydroelectric potential, contributing to the choice of hydroelectric power plants as the main technology of electricity generation in Brazil. Though this is a renewable source, whose utilized resource is free, dams must to be established which generates a high environmental and social impact. The objective of this study is to evaluate the impact caused by these dams through the use of environmental indexes. These indexes are ratio formed by installed power with dam area of a hydro power plant, and ratio formed by firm power with this dam area. In this study, the greatest media values were found in South, Southeast, and Northeast regions respectively, and the smallest media values were found in North and Mid-West regions, respectively. The greatest encountered media indexes were also found in dams established in the 1950s. In the last six decades, the smallest indexes were registered by darns established in the 1980s. These indexes could be utilized as important instruments for environmental impact assessments, and could enable a dam to be established that depletes an ecosystem as less as possible.

Genetic (co)variance components for ratio of lamb weight to ewe metabolic weight as an indicator of ewe efficiency

Data from a multibreed commercial flock located at Mid-West of Brazil, supported by Programa de Melhoramento Genético de Caprinos e Ovinos de Corte (GENECOC), were used to estimate genetic parameters of traits related to ewe productivity by Average Information Restricted Maximum Likelihood method applied to an animal model. The analyzed traits were litter weight at birth (LWB) and at weaning (LWW), ewe weight at weaning (EW) and ewe production efficiency, estimated by WEE=LWW/EW 0.75. The heritabilities were 0.26±0.05, 0.32±0.06, 0.37±0.03 and 0.10±0.02 for LWB, LWW, EW and WEE, respectively. Significant effects for direct heterosis were observed for LWW and EW. Recombination losses were important for EW and WEE. Genetic correlations of LWB with LWW, EW and WEE were 0.68, 0.37 and 0.15, respectively; of LWW with EW and WEE were 0.30 and 0.34, respectively; and between EW and WEE was -0.25. Even though it is a low heritability trait, WEE can be indicated as a selection criteria for improving the ewe productivity without increasing the mature weight of animals due to its genetic correlations with LWW and other traits. © 2011 Elsevier B.V.