Predicting survival function and identifying associated factors in patients with renal insufficiency in the metropolitan area of Maringá, Paraná State, Brazil

Renal insufficiency is a serious medical and public health problem worldwide. Recently, although many surveys have been developed to identify factors related to the lifetime of patients with renal insufficiency, controversial results from several studies suggest that researches should be conducted by region. Thus, in this study we aim to predict and identify factors associated with the lifetime of patients with chronic renal failure (CRF) in the metropolitan area of Maringá, Paraná State, Brazil, based on the generalized additive models for location, scale and shape (GAMLSS) framework. Data used in this study were collected from the Maringá Kidney Institute and comprehends 177 patients (classified with CRF and mostly being treated under the Brazilian Unified National Health System) enrolled in a hemodialysis program from 1978 up to 2010. By using this approach, we concluded that in other regions, gender, kidney transplant indicator, antibodies to hepatitis B and antibodies to hepatitis C are significant factors that affect the expected lifetime

Comparison between Highly Complex Location Models and GAMLSS

This paper presents a discussion regarding regression models, especially those belonging to the location class. Our main motivation is that, with simple distributions having simple interpretations, in some cases, one gets better results than the ones obtained with overly complex distributions. For instance, with the reverse Gumbel (RG) distribution, it is possible to explain response variables by making use of the generalized additive models for location, scale, and shape (GAMLSS) framework, which allows the fitting of several parameters (characteristics) of the probabilistic distributions, like mean, mode, variance, and others. Three real data applications are used to compare several location models against the RG under the GAMLSS framework. The intention is to show that the use of a simple distribution (e.g., RG) based on a more sophisticated regression structure may be preferable than using a more complex location model

Building flexible regression models: including the Birnbaum-Saunders distribution in the gamlss package

Generalized additive models for location, scale and shape (GAMLSS) are a very flexible statistical modeling framework, being an important generalization of the well-known generalized linear models and generalized additive models. Their main advantage is that any probability distribution (that does not necessarily belong to the exponential family) can be considered to model the response variable and different regression structures can be fitted in each of its parameters. Currently, there are more than 100 distributions that are already implemented in the gamlss package in R software. Nevertheless, researchers can implement different distributions if they are not yet available, e.g., the Birnbaum-Saunders (BS) distribution, which is widely used in fatigue studies. In this paper we make available all codes regarding the inclusion of the BS distribution in the gamlss package, and then present a simple application related to air quality data for illustration purpose

Predicting weed invasion in a sugarcane cultivar using multispectral image

<p>The cultivation of sugar cane has been gaining great focus in several countries due to its diversity of use. The modernization of agriculture has allowed high productivity, which is affected by the invasion of weeds. With sustainable agriculture, the use of herbicides has been increasingly avoided in society, requiring more effective weed control methods. In this paper, we propose a statistical model capable of identifying the invasion of weeds in the field, using four color spectra as regressor variables obtained by a multispectral camera mounted on an unmanned aerial vehicle. With the exact identification of the weed infestation, it is possible to carry out the management in the field with herbicide applications in the exact places, thus avoiding the increase of the cost of production or even dispensing with the use of herbicides, effecting the mechanical removal of them. Results show that in the experimental field, it was possible to reduce herbicide spraying by 57%.</p