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Binary logistic regression as an instrument for the prediction of immunological deterioration from nutritional indicators in persons infected with HIV-AIDS. In the evolution of infection with HIV diverse markers of progression have been used, of immunological, virological and genetic type. However, the significance of nutritional indicators for prognosis has been insufficiently studied. The objective of this investigation was to use binary logistic regression as a tool to find a mathematical model which would allow to predict the counting of CD4+ cells in persons infected with HIV-AIDS after a year's follow-up. 118 persons with HIV-AIDS were studied (initial time and after a year of evolution) which were classified in four subgroups according to the use or not use of antirretrovirals and the counting of CD4 at the initial time. Nutritional, biochemical and anthropometric variables were used, as well as the gradients between both observations. Binary logistic regression was applied, having as dependent variable the counting of CD4 as an immunological criterion of antirretroviral therapy. The logistical models found were adjusted according to the subgroup they belonged to, and they were compared to one another according to the goodness of fit and accuracy. The step-by-step method chose only the gradient of body mass index (IMC) as independent variable. The adjustment of the model allowed to propose a mathematical equation by subgroup in order to predict the need of antirretroviral therapy after a year. The groups which had initial values of CD4 < 350, even with an increase in IMC ran the risk of keeping immunological deterioration after a year. All models showed specificity values higher than those of sensitiveness, though models II and III were the best, as proved by the probabilities shown in ROC curves. The conclusion is that the IMC gradient in a year of evolution might be used in a logistical mathematical model to predict the need of antirretroviral therapy in individuals with HIV-AIDS. [ABSTRACT FROM AUTHOR]

In this work we have collected estimators of the population proportion through the use of auxiliary information under simple random sampling. We analyzed the main properties of these estimators. Theorethical properties suggest that the estimators with use of auxiliary information can outperform alternative methods, and the results derived from a Monte Carlo simulation study support this view. Has made an application on a population of Risk of Acidication in Lakes in the Northeastern states of the U.S. conducted by the Environmental Monitoring and Assessment Program of the US Environmental Protection Agency. In the application we have compared the different estimators each other with the standard stimators. [ABSTRACT FROM AUTHOR]

The technique of Randomized Response has been introduced to reduce the risk of evasive answer or no-response in survey samplings of sensitive issues to estimate the total of individuals with that sensitive characteristic. We also know that highly efficient strategies of estimation require strong auxiliary information. Hence, in this article we obtain and compare two estimators: The Rao-Hartley-Cochran (RHC) estimator with Warner's model and the RHC estimator with Greenberg's model, both in a finite population setting. We will show via simulation that ŧA1,RHC,π the RHC π-estimator in Warner's model is less efficient than ŧA1,RHC,π, the RHC π-estimator in Greenberg's model. [ABSTRACT FROM AUTHOR]

The majority of the developments in a classic problem of competing risks have consisted in, based on the assumption of independence between all event types, make estimations to several rates. In this paper, we propose an adjusted Life-Table estimator in the context of non-independent competing risks. We proof that the proposed estimator is maximum pseudo-likelihood. We compare the adjusted and classic estimators by simulation study. It confirmed that de adjusted estimator provides closer estimations to the real rate than the classic estimator. [ABSTRACT FROM AUTHOR]