Expanded renal transplantation: a competing risk model approach

Multi-state models (MSMs) are useful to analyze survival data when, besides the event of main interest, one or more intermediate states of the individual are identified. These models take the several existing states and the possible transitions among them into account. At the same time, covariate effects on each transition intensity may be investigated separately and, therefore, MSMs are more flexible than the standard Cox proportional hazards model. In this work, we use MSMs to investigate the impact of the quality of a transplanted kidney for a group of patients at the Hospital Universitario Central de Asturias. Specifically, we use an illness-death model to study the evolution of patients with kidney disease who received a renal transplant after a dialysis period. The intermediate state is defined as the failure of the received organ, while the terminating state is the death of the patient. In order to increase the potential number of organs available for transplant, the standards of quality for the transplanted kidneys were relaxed (the new criteria are labeled expanded criteria), and these ‘expanded kidneys’ were transplanted in appropriate candidates (older patients, with higher prevalence of diabetes mellitus). Results suggest that the expanded kidneys have a minor effect on survival, while both the kidney mortality and the risk of death increase with the patient's age and the serum creatinine and serum hemoglobin levels.     

Immigrants and refugees: two sides of the same problem. A linguistic analysis through newspapers and social network in Spain 2006 and 2015

ABSTRACT

The phenomenon of immigration has been studied from diverse perspectives. It is important to note that immigration is common during times of crisis. The reason for the avalanche of immigrants to the Canary Islands (Spain) is because it is the gateway to Europe, and therefore, immigrants wanted to enter from this point. This research is based on the need to linguistically determine the treatment of the phenomenon of immigration in the Spanish press. This analysis is relevant given the arrival of thousands of foreign citizens to the coast of the Canary Islands, first in 2006 and almost a decade later, in 2015 to the south of Europe in another great population movement. This research attempts to study four Spanish newspapers, two from the Canary Islands (El Día and Canarias 7) and two Spanish mainland national newspapers (El País and ABC), the online version of the newspapers and their websites were analyzed beside one of the most important social networks, Twitter. The role of incorporating social networks to immigration discourse is also analyzed. To support the data analysis of this research, the Iramuteq software was used.     

On the asymptotic distribution of Cook's distance in logistic regression models

It sometimes occurs that one or more components of the data exert a disproportionate influence on the model estimation. We need a reliable tool for identifying such troublesome cases in order to decide either eliminate from the sample, when the data collect was badly realized, or otherwise take care on the use of the model because the results could be affected by such components. Since a measure for detecting influential cases in linear regression setting was proposed by Cook [Detection of influential observations in linear regression, Technometrics 19 (1977), pp. 15–18.], apart from the same measure for other models, several new measures have been suggested as single-case diagnostics. For most of them some cutoff values have been recommended (see [D.A. Belsley, E. Kuh, and R.E. Welsch, Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, 2nd ed., John Wiley & Sons, New York, Chichester, Brisban, (2004).], for instance), however the lack of a quantile type cutoff for Cook's statistics has induced the analyst to deal only with index plots as worthy diagnostic tools. Focussed on logistic regression, the aim of this paper is to provide the asymptotic distribution of Cook's distance in order to look for a meaningful cutoff point for detecting influential and leverage observations.     

The Importance of the Coparental Relationship for Nonresident Fathers’ Ties to Children

We examine the importance of the coparental relationship for nonresident fathers’ ties to their children. Using data from Wave 2 of the National Survey of Families and Households, we focus on the link between two dimensions of coparenting, cooperative coparenting and conflict over childrearing, and three dimensions of nonresident father involvement, contact, relationship quality, and responsive fathering. Cooperative coparenting predicts more frequent father‐child contact, which in turn predicts higher relationship quality and more responsive fathering. Conflict over childrearing, however, is not significantly related to nonresident father involvement. Findings are consistent across different groups of children. Results suggest that cooperative coparenting between parents who live apart is associated with stronger ties between nonresident fathers and their children.     

Spiritual Needs and Quality of Life in Estonia

This paper reports on three field studies using the WHOQOL-100 and WHOQOL-BREF instruments that utilized three different samples (N = 1,801) to get a better understanding of how important the person’s spiritual needs are for quality of life. The most striking negative difference between the Estonian and World Health Organization samples was in the WHOQOL-100 spirituality domain. We found that the quality of life index significantly correlated with the WHOQOL-100 spirituality score. Also, spirituality was related to all quality of life domains (physical health, psychological well-being, level of independence, social relationships and environment). Regarding psychological well-being, spirituality correlated with self-esteem, positive feelings, and thinking, learning, memory, and concentration, on the other. Our findings suggest that spirituality occupies an important place in the person’s perception of their quality of life in a changing socio–economic environment as the one in Estonia.     

A-optimal designs for heteroscedastic multifactor regression models

Abstract

This paper searches for A-optimal designs for Kronecker product and additive regression models when the errors are heteroscedastic. Sufficient conditions are given so that A-optimal designs for the multifactor models can be built from A-optimal designs for their sub-models with a single factor. The results of an efficiency study carried out to check the adequacy of the products of optimal designs for uni-factor marginal models when these are used to estimate different multi-factor models are also reported.     

Inference in log-alpha-power and log-skew-normal multivariate models

ABSTRACT

Random vectors with positive components are common in many applied fields, for example, in meteorology, when daily precipitation is measured through a region Marchenko and Genton (2010 Marchenko, Y., Genton, M. (2010). Multivariate log-skew-elliptical distributions with applications to precipitation data. Environmetrics 21:318–340.[Crossref], [Web of Science ®] , [Google Scholar]). Frequently, the log-normal multivariate distribution is used for modeling this type of data. This modeling approach is not appropriate for data with high asymmetry or kurtosis. Consequently, more flexible multivariate distributions than the log-normal multivariate are required. As an alternative to this distribution, we propose the log-alpha-power multivariate and log-skew-normal multivariate models. The first model is an extension for positive data of the fractional order statistics model Durrans (1992 Durrans, S. (1992). Distributions of fractional order statistics in hydrology. Water Resour. Res. 28:1649–1655.[Crossref], [Web of Science ®] , [Google Scholar]). The second one is an extension of the log-skew-normal model studied by Mateu-Figueras and Pawlowsky-Glahn (2007 Mateu-Figueras, G., Pawlowsky-Glahn, V. (2007). The skew-normal distribution on the simplex. Commun. Stat.-Theory Methods 36:1787–1802.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]). We study parameter estimation for these models by means of pseudo-likelihood and maximum likelihood methods. We illustrate the proposal analyzing a real dataset.     

On asymptotic posterior normality for controlled branching processes

For a controlled branching process (CBP) with offspring distribution belonging to the power series family, the asymptotic normality of the posterior distribution of the basic parameter and the offspring mean is proved. As practical applications, we calculate asymptotic high probability density credibility sets for the offspring mean and we provide a rule to make inference about the value of this parameter. Moreover, the asymptotic posterior normality of the respective parameters of two classical branching models, namely the standard Galton–Watson process and the Galton–Watson process with immigration, is derived as particular cases of the CBP.     

Hypothesis test for paired samples in the presence of missing data

Missing data are present in almost all statistical analysis. In simple paired design tests, when some subject has one of the involved variables missing in the so-called partially overlapping samples scheme, it is usually discarded for the analysis. The lack of consistency between the information reported in the univariate and multivariate analysis is, perhaps, the main consequence. Although the randomness on the missing mechanism (missingness completely at random) is an usual and needed assumption for this particular situation, missing data presence could lead to serious inconsistencies on the reported conclusions. In this paper, the authors develop a simple and direct procedure which allows using the whole available information in order to perform paired tests. In particular, the proposed methodology is applied to check the equality among the means from two paired samples. In addition, the use of two different resampling techniques is also explored. Finally, real-world data are analysed.     

Confidence Intervals for the Hyperparameters in Structural Models

This article deals with the bootstrap as an alternative method to construct confidence intervals for the hyperparameters of structural models. The bootstrap procedure considered is the classical nonparametric bootstrap in the residuals of the fitted model using a well-known approach. The performance of this procedure is empirically obtained through Monte Carlo simulations implemented in Ox. Asymptotic and percentile bootstrap confidence intervals for the hyperparameters are built and compared by means of the coverage percentages. The results are similar but the bootstrap procedure is better for small sample sizes. The methods are applied to a real time series and confidence intervals are built for the hyperparameters.