A general class of promotion time cure rate models with a new biological interpretation.

Over the last decades, the challenges in survival models have been changing considerably and full probabilistic modeling is crucial in many medical applications. Motivated from a new biological interpretation of cancer metastasis, we introduce a general method for obtaining more flexible cure rate models. The proposal model extended the promotion time cure rate model. Furthermore, it includes several well-known models as special cases and defines many new special models. We derive several properties of the hazard function for the proposed model and establish mathematical relationships with the promotion time cure rate model. We consider a frequentist approach to perform inferences, and the maximum likelihood method is employed to estimate the model parameters. Simulation studies are conducted to evaluate its performance with a discussion of the obtained results. A real dataset from population-based study of incident cases of melanoma diagnosed in the state of São Paulo, Brazil, is discussed in detail.

On a new piecewise regression model with cure rate: Diagnostics and application to medical data.

In this article, we discuss an extension of the classical negative binomial cure rate model with piecewise exponential distribution of the time to event for concurrent causes, which enables the modeling of monotonic and non-monotonic hazard functions (ie, the shape of the hazard function is not assumed as in traditional parametric models). This approach produces a flexible cure rate model, depending on the choice of time partition. We discuss local influence on this negative binomial power piecewise exponential model. We report on Monte Carlo simulation studies and application of the model to real melanoma and leukemia datasets.

Anti-inflammatory effect and inhibition of nitric oxide production by targeting COXs and iNOS enzymes with the 1,2-diphenylbenzimidazole pharmacophore.

Being the base of several non-communicable diseases, including cancer, inflammation is a complex process generated by tissue damage or change in the body homeostatic state. Currently, the therapeutic treatment for chronic inflammation related diseases is based on the use of selective cyclooxygenase II enzyme, COX-2, inhibitors or Coxibs, which have recently regained attention giving their preventive role in colon cancer. Thus, the discovery of new molecules that selectively inhibit COX-2 and other inflammatory mediators is a current challenge in the medicinal chemistry field. 1-Phenylbenzimidazoles have shown potential COX inhibitory activity, because they can reproduce the interaction profile of known COX inhibitors. Therefore, in the present investigation a series of 1,2-diphenylbenzimidazoles (DPBI) with different aromatic substitutions in the para position were synthesized and their interaction with COX-2 and nitric oxide synthase, iNOS, was determined in silico, in vitro and in vivo. Compound 2-(4-bromophenyl)-1-(4-nitrophenyl)-1H-benzo[d]imidazole showed the best inhibition towards COX-2, while compounds N-(4-(2-(4-bromophenyl)-1H-benzo[d]imidazol-1-yl)phenyl)acetamide and N-(4-(2-(4-chlorophenyl)-1H-benzo[d]imidazol-1-yl)phenyl)acetamide diminished the production of NO in vitro. Additionally, they had a significant anti-inflammatory activity in vivo when given orally.

On the use of the modified power series family of distributions in a cure rate model context.

In this paper, we propose a generalization of the power series cure rate model for the number of competing causes related to the occurrence of the event of interest. The model includes distributions not yet used in the cure rate models context, such as the Borel, Haight and Restricted Generalized Poisson distributions. The model is conveniently parameterized in terms of the cure rate. Maximum likelihood estimation based on the Expectation Maximization algorithm is discussed. A simulation study designed to assess some properties of the estimators is carried out, showing the good performance of the proposed estimation procedure in finite samples. Finally, an application to a bone marrow transplant data set is presented.