Applying Spatial Copula Additive Regression to Breast Cancer Screening Data

Breast cancer is associated with several risk factors. Although genetics is an important breast cancer risk factor, environmental and sociodemographic characteristics, that may differ across populations, are also factors to be taken into account when studying the disease. These factors, apart from having a role as direct agents in the risk of the disease, can also influence other variables that act as risk factors. The age at menarche and the reproductive lifespan are considered by the literature as breast cancer risk factors so that, there are several studies whose aim is to analyze the trend of age at menarche and menopause along generations. Also, it is believed that these two moments in a woman’s life can be affected by environmental, social status, and lifestyles of women. Using the information of 278,282 registries of women which entered in the breast cancer screening program in Central Portugal, we developed a bivariate copula model to quantify the effect a woman’s year of birth in the association between age at menarche and a woman’s reproductive lifespan, in addition to explore any possible effect of the geographic location in these variables and their association. For this analysis we employ Copula Generalized Additive Models for Location, Scale and Shape (CGAMLSS) models and the inference was carried out using the R package SemiParBIVProbit

Interaction between Attention and Bottom-Up Saliency Mediates the Representation of Foreground and Background in an Auditory Scene

Bottom-up (stimulus-driven) and top-down (attentional) processes interact when a complex acoustic scene is parsed. Both modulate the neural representation of the target in a manner strongly correlated with behavioral performance

On the censored cost-effectiveness analysis using copula information

Abstract Background Information and theory beyond copula concepts are essential to understand the dependence relationship between several marginal covariates distributions. In a therapeutic trial data scheme, most of the time, censoring occurs. That could lead to a biased interpretation of the dependence relationship between marginal distributions. Furthermore, it could result in a biased inference of the joint probability distribution function. A particular case is the cost-effectiveness analysis (CEA), which has shown its utility in many medico-economic studies and where censoring often occurs. Methods This paper discusses a copula-based modeling of the joint density and an estimation method of the costs, and quality adjusted life years (QALY) in a cost-effectiveness analysis in case of censoring. This method is not based on any linearity assumption on the inferred variables, but on a punctual estimation obtained from the marginal distributions together with their dependence link. Results Our results show that the proposed methodology keeps only the bias resulting statistical inference and don’t have anymore a bias based on a unverified linearity assumption. An acupuncture study for chronic headache in primary care was used to show the applicability of the method and the obtained ICER keeps in the confidence interval of the standard regression methodology. Conclusion For the cost-effectiveness literature, such a technique without any linearity assumption is a progress since it does not need the specification of a global linear regression model. Hence, the estimation of the a marginal distributions for each therapeutic arm, the concordance measures between these populations and the right copulas families is now sufficient to process to the whole CEA

Analysis of paediatric visual acuity using Bayesian copula models with sinh-arcsinh marginal densities

We analyse paediatric ophthalmic data from a large sample of children aged between 3 and 8 years. We modify the Bayesian additive conditional bivariate copula regression model of Klein and Kneib [1] by using sinh-arcsinh marginal densities with location, scale and shape parameters that depend smoothly on a covariate. We perform Bayesian inference about the unknown quantities of our model using a specially tailored Markov chain Monte Carlo algorithm. We gain new insights about the processes which determine transformations in visual acuity with respect to age, including the nature of joint changes in both eyes as modelled with the age-related copula dependence parameter. We analyse posterior predictive distributions to identify children with unusual sight characteristics, distinguishing those who are bivariate, but not univariate outliers. In this way we provide an innovative tool that enables clinicians to identify children with unusual sight who may otherwise be missed. We compare our simultaneous Bayesian method with the two-step frequentist generalized additive modelling approach of Vatter and Chavez-Demoulin [2]