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Traditionally the diagnosis of Metabolic syndrome (MetS) is binary (present/absent). The goal of this work is to propose a sex-specific continuous score to measure the severity of MetS in Mexican adults using waist circumference and body mass index as adiposity measures. MetSx-WC and MetSx-BMI indexes by sex were derived by confirmatory factor analysis (CFA) using data for 6567 adult participants of the National Health and Nutrition Survey 2018. The overall fit of the two proposed CFA models was excellent. We then validated these scores using a community-based health study of 862 university participants and determined that the reliability and strength of agreement between the MetSx-WC and MetSx-BMI scores were excellent. The ROC analysis of the resulting indexes indicates that they have excellent ability to discriminate a MetS classification according to the different criteria. The correlations of MetSx scores and surrogate markers of insulin resistance and obesity ranged from weak to strong. Subsequently, a retrospective study of 310 hospitalized patients with COVID-19 was used to determined that MetSx-BMI score was associated with the mortality of patients with COVID-19. The proposed indices provide a continuous measure in the identification of MetS risk in Mexican adults. [ABSTRACT FROM AUTHOR]

In this work, a simulation study is conducted to evaluate the performance of Bayesian estimators for the log–linear exponential regression model under different levels of censoring and degrees of collinearity for two covariates. The diffuse normal, independent Student-t and multivariate Student-t distributions are considered as prior distributions and to draw from the posterior distributions, the Metropolis algorithm is implemented. Also, the results are compared with the maximum likelihood estimators in terms of the mean squared error, coverages and length of the credibility and confidence intervals. [ABSTRACT FROM PUBLISHER]

Collinearity and the lack of overlap in the data are problems that affect inference based on the logistic regression model. Simulation was used to investigate how the estimators that deal with collinearity (iterative Ridge) are affected, along with separation in the data (Firth's, and Rousseeuw and Christmann's) or both problems (Shen and Gao's). These estimators were compared considering the scaled condition number of the estimated information matrix, the bias and the mean squared error. In each one of the four scenarios studied, formed by using two levels of collinearity and two sample sizes, three degrees of overlap were considered in the data. It was found that iterative Ridge and Shen and Gao's estimators have null conditioning, as well as smaller bias and mean square error. The degree of overlap and the level of collinearity strongly affect the bias and mean square error of the maximum likelihood, Firth's and Rousseeuw and Christmann's estimators. [ABSTRACT FROM AUTHOR]