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The class of semiparametric or transformation models has been presented in the literature as a promising alternative for the analysis of lifetime data in the presence of covariates and censored data. This class of models generalizes the popular class of proportional hazards models proposed by Cox (J R Stat Soc: Ser B (Methodol) 34(2):187–202, 1972) where it is not needed to assume a parametric probability distribution for the survival times. In addition to our focus on semipametric models, we also explore the situation where the population of interest is a mixture of susceptible individuals, who experience the event of interest and non-susceptible individuals that will never experience the event of interest. These individuals are not at risk with respect to the event of interest and are considered immune, non-susceptible, or cured. In this study, we present a simple method to obtain inferences for the parameters of semiparametric or transformation models in the presence of censoring, covariates and cure fraction under a Bayesian approach assuming the unknown hazard rates as latent variables with a given probability distribution. The posterior summaries of interest are obtained using existing Markov Chain Monte Carlo (MCMC) simulation methods. Some applications with real medical survival data illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

The topography of a biomaterial can influence the biological behavior of cells cultivated on its surface, with effects on cell proliferation, viability, and differentiation. In this study, investigation was made of physicochemical effects of the use of oxygen plasma to modify the surface of a bacterial cellulose platform for cell culture. Two durations of exposure (2.5 and 5 min) of the bacterial cellulose platforms to plasma were used, with analysis of the effects on the microenvironment of the OSTEO-1 cells cultivated for 14 days on the surface of the platforms. The physicochemical modifications were evaluated using Fourier transform infrared spectroscopy with attenuated total reflectance, contact angle measurements, and atomic force microscopy. The biological behavior of the OSTEO-1 cells was evaluated considering cell proliferation, cell viability, degree of mineralization, and alkaline phosphatase activity. The contact angle and atomic force microscopy results revealed time-dependent changes in the surface roughness and polarity of the samples, which were responsible for modulation of the biological responses of the cells cultivated on the cellulose platforms. Plasma modification resulted in greater hydrophobicity of the sample surfaces. The changes in the roughness of the bacterial cellulose platforms indicated that the physical modifications induced by the oxygen plasma treatment provided a favorable microenvironment for the cultured cells. [ABSTRACT FROM AUTHOR]

In this study, non-homogeneous Poisson processes (NHPP) are used to analyze climate data. The data were collected over a certain period time and consist of the yearly average precipitation, yearly average temperature and yearly average maximum temperature for some regions of the world. Different existing parametric forms depending on time and on unknown parameters are assumed for the intensity/rate function λ (t) , t ≥ 0 of the NHPP. In the present context, the Poisson events of interest are the numbers of years that a climate variable measurement has exceeded a given threshold of interest. The threshold corresponds to the overall average measurements of each climate variable taking into account here. Two versions of the NHPP model are considered in the study, one version without including change points and one version including a change point. The parameters included in the model are estimated under a Bayesian approach using standard Markov chain Monte Carlo (MCMC) methods such as the Gibbs sampling and Metropolis–Hastings algorithms. The models are applied to climate data from Kazakhstan and Uzbekistan, in Central Asia and from the USA obtained over several years. [ABSTRACT FROM AUTHOR]

Summary: Binary responses are correlated when the sampling units are clustered or when repeated binary responses are taken on the same experiment unit. In this paper we present a Bayesian analysis of logistic regression models for correlated binary data with random effects. We assume that the random effects, namely αi, i = 1, ..., n are draw from a mixture of normal distributions. This assumption gives a great flexibility of fit by correlated binary data. Considering Gibbs sampling with Metropolis-Hastings algorithms, we obtain Monte Carlo estimates for the posterior quantities of interest [ABSTRACT FROM AUTHOR]

Summary: In this paper, we present a Bayesian analysis of mixture models for survival data in the presence of one covariate, and type II censoring data. Considering Gibbs with Metropolis-Hastings algorithms, we get Monte Carlo estimates for the posterior quantities of interest, assuming different choices for the densities in the mixture model. We introduce a numerical example, to illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]