Feature sensitivity criterion-based sampling strategy from the Optimization based on Phylogram Analysis (Fs-OPA) and Cox regression applied to mental disorder datasets.

Digital datasets in several health care facilities, as hospitals and prehospital services, accumulated data from thousands of patients for more than a decade. In general, there is no local team with enough experts with the required different skills capable of analyzing them in entirety. The integration of those abilities usually demands a relatively long-period and is cost.

Semiparametric Bayesian estimation of quantile function for breast cancer survival data with cured fraction.

Existing cure-rate survival models are generally not convenient for modeling and estimating the survival quantiles of a patient with specified covariate values. This paper proposes a novel class of cure-rate model, the transform-both-sides cure-rate model (TBSCRM), that can be used to make inferences about both the cure-rate and the survival quantiles. We develop the Bayesian inference about the covariate effects on the cure-rate as well as on the survival quantiles via Markov Chain Monte Carlo (MCMC) tools.

A mechanistic breast cancer survival modelling through the axillary lymph node chain.

In this paper, we proposed a mechanistic breast cancer survival model based on the axillary lymph node chain structure, considering lymph nodes as a potential dissemination arrangement. We assume a naive breast cancer treatment protocol consisting of exposing patients first to a chemotherapy treatment on r intervals at k-cycles separated by equal time intervals, and then they proceed to surgery. Our model, different from former ones, accommodates a quantity of contaminated lymph nodes, which is observed during surgery.

A Bayesian approach for decision making on the identification of genes with different expression levels: an application to Escherichia coli bacterium data.

A common interest in gene expression data analysis is to identify from a large pool of candidate genes the genes that present significant changes in expression levels between a treatment and a control biological condition. Usually, it is done using a statistic value and a cutoff value that are used to separate the genes differentially and nondifferentially expressed. In this paper, we propose a Bayesian approach to identify genes differentially expressed calculating sequentially credibility intervals from predictive densities which are constructed using the sampled mean treatment effect from all genes in study excluding the treatment effect of genes previously identified with statistical evidence for difference.