A Bayesian Long-term Survival Model Parametrized in the Cured Fraction

The main goal of this paper is to investigate a cure rate model that comprehends some well-known proposals found in the literature. In our work the number of competing causes of the event of interest follows the negative binomial distribution. The model is conveniently reparametrized through the cured fraction, which is then linked to covariates by means of the logistic link. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis in the proposed model. The procedure is illustrated with a numerical example.

Bayesian analysis for a skew extension of the multivariate null intercept measurement error model

Skew-normal distribution is a class of distributions that includes the normal distributions as a special case. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis in a multivariate, null intercept, measurement error model [R. Aoki, H. Bolfarine, J.A. Achcar, and D. Leao Pinto Jr, Bayesian analysis of a multivariate null intercept error-in -variables regression model, J. Biopharm. Stat. 13(4) (2003b), pp. 763-771] where the unobserved value of the covariate (latent variable) follows a skew-normal distribution. The results and methods are applied to a real dental clinical trial presented in [A. Hadgu and G. Koch, Application of generalized estimating equations to a dental randomized clinical trial, J. Biopharm. Stat. 9 (1999), pp. 161-178].

Bayesian Estimation of the Logistic Positive Exponent IRT Model

A Bayesian inference approach using Markov Chain Monte Carlo (MCMC) is developed for the logistic positive exponent (LPE) model proposed by Samejima and for a new skewed Logistic Item Response Theory (IRT) model, named Reflection LPE model. Both models lead to asymmetric item characteristic curves (ICC) and can be appropriate because a symmetric ICC treats both correct and incorrect answers symmetrically, which results in a logical contradiction in ordering examinees on the ability scale. A data set corresponding to a mathematical test applied in Peruvian public schools is analyzed, where comparisons with other parametric IRT models also are conducted. Several model comparison criteria are discussed and implemented. The main conclusion is that the LPE and RLPE IRT models are easy to implement and seem to provide the best fit to the data set considered.

Semiparametric Bayesian measurement error modeling

This work presents a Bayesian semiparametric approach for dealing with regression models where the covariate is measured with error. Given that (1) the error normality assumption is very restrictive, and (2) assuming a specific elliptical distribution for errors (Student-t for example), may be somewhat presumptuous; there is need for more flexible methods, in terms of assuming only symmetry of errors (admitting unknown kurtosis). In this sense, the main advantage of this extended Bayesian approach is the possibility of considering generalizations of the elliptical family of models by using Dirichlet process priors in dependent and independent situations. Conditional posterior distributions are implemented, allowing the use of Markov Chain Monte Carlo (MCMC), to generate the posterior distributions. An interesting result shown is that the Dirichlet process prior is not updated in the case of the dependent elliptical model. Furthermore, an analysis of a real data set is reported to illustrate the usefulness of our approach, in dealing with outliers. Finally, semiparametric proposed models and parametric normal model are compared, graphically with the posterior distribution density of the coefficients. (C) 2009 Elsevier Inc. All rights reserved.

A Bayesian Semiparametric Approach for Solving the Discrete Calibration Problem

In this article, we introduce a semi-parametric Bayesian approach based on Dirichlet process priors for the discrete calibration problem in binomial regression models. An interesting topic is the dosimetry problem related to the dose-response model. A hierarchical formulation is provided so that a Markov chain Monte Carlo approach is developed. The methodology is applied to simulated and real data.

NONSTATIONARY MIXING AND THE UNIQUE ERGODICITY OF ADIC TRANSFORMATIONS

We define topological and measure-theoretic mixing for nonstationary dynamical systems and prove that for a nonstationary subshift of finite type, topological mixing implies the minimality of any adic transformation defined on the edge space, while if the Parry measure sequence is mixing, the adic transformation is uniquely ergodic. We also show this measure theoretic mixing is equivalent to weak ergodicity of the edge matrices in the sense of inhomogeneous Markov chain theory.

Development of secondary palate requires strict regulation of ECM remodeling: sequential distribution of RECK, MMP-2, MMP-3, and MMP-9

We have evaluated RECK (reversion-inducing-cysteine-rich protein with Kazal motifs), MMP-2 (matrix metalloproteinase-2), MMP-3, and MMP-9 involvement during palate development in mice by using various techniques. Immunohistochemical features revealed the distribution of RECK, MMP-2, and MMP-3 in the mesenchymal tissue and in the midline epithelial seam at embryonic day 13 (E13), MMPs-2, -3, and -9 being particularly expressed at E14 and E14.5. In contrast, RECK was weakly immunostained at these times. Involvement of MMPs was validated by measuring not only their protein expression, but also their activity (zymograms). In situ hybridization signal (ISH) for RECK transcript was distributed in mesenchymal and epithelial regions within palatal shelves at all periods evaluated. Importantly, the results from ISH analysis were in accord with those obtained by real-time polymerase chain reaction. The expression of RECK was found to be temporally regulated, which suggested possible roles in palatal ontogeny. Taken together, our results clearly show that remodeling of the extracellular matrix is finely modulated during secondary palate development and occurs in a sequential manner.

Expression of human papillomavirus type 16 E7 oncoprotein alters keratinocytes expression profile in response to tumor necrosis factor-alpha

Acute expression of E7 oncogene from human papillomavirus (HPV) 16 or HPV18 is sufficient to overcome tumor necrosis factor (TNF)-alpha cytostatic effect on primary human keratinocytes. In the present study, we investigated the molecular basis of E7-induced TNF resistance through a comparative analysis of the effect of this cytokine on the proliferation and global gene expression of normal and E7-expressing keratinocytes. Using E7 functional mutants, we show that E7-induced TNF resistance correlates with its ability to mediate pRb degradation and cell transformation. On the other hand, this effect does not depend on E7 sequences required to override DNA damage-induced cell cycle arrest or extend keratinocyte life span. Furthermore, we identified a group of 66 genes whose expression pattern differs between normal and E7-expressing cells upon cytokine treatment. These genes are mainly involved in cell cycle regulation suggesting that their altered expression may contribute to sustained cell proliferation even in the presence of a cytostatic stimulus. Differential expression of TCN1 (transcobalamin I), IFI44 (Interferon-induced protein 44), HMGB2 (high-mobility group box 2) and FUS [Fusion (involved in t(12; 16) in malignant liposarcoma)] among other genes were further confirmed by western-blot and/or real-time polymerase chain reaction. Moreover, FUS upregulation was detected in HPV-positive cervical high-grade squamous intraepithelial lesions when compared with normal cervical tissue. Further evaluation of the role of such genes in TNF resistance and HPVassociated disease development is warranted.