The postmastectomy survival rates are often based on previous outcomes of large numbers of women who had a disease, but they do not accurately predict what will happen in any particular patient's case. Pathologic explanatory variables such as disease multifocality, tumor size, tumor grade, lymphovascular invasion, and enhanced lymph node staining are prognostically significant to predict these survival rates. We propose a new cure rate survival regression model for predicting breast carcinoma survival in women who underwent mastectomy. We assume that the unknown number of competing causes that can influence the survival time is given by a power series distribution and that the time of the tumor cells left active after the mastectomy for metastasizing follows the beta Weibull distribution. The new compounding regression model includes as special cases several well-known cure rate models discussed in the literature. The model parameters are estimated by maximum likelihood. Further, for different parameter settings, sample sizes, and censoring percentages, some simulations are performed. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess local influences. The potentiality of the new regression model to predict accurately breast carcinoma mortality is illustrated by means of real data.

Human organs for transplantation are extremely valuable goods and their shortage is a problem that has been verified in most countries around the world, generating a long waiting list for organ transplants. This is one of the most pressing health policy issues for governments. To deal with this problem, some researchers have suggested a change in organ donation law, from informed consent to presumed consent. However, few empirical works have been done to measure the relationship between presumed consent and the number of organ donations. The aim of this paper is to estimate that impact, using Health expenditure has an important role on the response variable as well, the coefficient estimate varying between 42-52%.

Summary: 1. The issue; 2. The data; 3. Unit root tests; 4. Measuring inflation inertia; 5. Some simulation results; 6. Concluding remarks and discussion.Keywords: inflation inertia; inliers; persistence; unit roots. JEL codes: C22; C12; C15. This paper addresses the issue of measuring the degree of inertia in inflation in the presence of potential 'inliers'. It shows that by using robust unit root tests one reaches the same inference on the order of integration of the series as what is revealed by the modified procedure proposed by Cati et al. (1999). The results also suggest that, contrary to previous findings, the degree of inertia in inflation is rather small. Finally, the paper presents simulation results on the finite-sample behavior of unit root tests and of a persistence measure when the data contain inliers.Este artigo analisa a mensuração do grau de inércia na inflação na presença de potenciais inliers. O artigo mostra que testes robustos de raízes unitárias conduzemà mesma inferência sobre a ordem de integração da série do que o procedimento modificado proposto por Cati et al. (1999). Os resultados sugerem que o grau de inércia na inflação brasileiraé pequeno. Por fim, o artigo apresenta resultados de simulação de Monte Carlo sobre o desempenho em amostras finitas de testes de raízes unitárias e de uma medida de persistência quando os dados contêm inliers.

We propose a new two-parameter continuous model called the extended arcsine distribution restricted to the unit interval. It is a very competitive model to the beta and Kumaraswamy distributions for modeling percentages, rates, fractions and proportions. We provide a mathematical treatment of the new distribution including explicit expressions for the ordinary and incomplete moments, mean deviations, Bonferroni and Lorenz curves, generating and quantile functions, Shannon entropy and order statistics. Maximum likelihood is used to estimate the model parameters and the expected information matrix is determined. We demonstrate by means of two applications to proportional data that it can give consistently a better fit than other important statistical models.

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