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

Ramires, Thiago G.; Nakamura, Luiz Ricardo; Righetto, Ana Julia; Ortega, Edwin M. M.; Cordeiro, Gauss M.

doi:10.1590/0102-311x00075517

Cited by 2 publications

(2 citation statements)

References 16 publications

(17 reference statements)

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“…The data analysed in this paper were collected from the Maringá Kidney Institute, for 177 patients (80 women and 97 men) classified with chronic renal failure enrolled in a hemodialysis program from 1978 up to 2010, and it was provided by Ramires et al (2018). As specified in the authors' paper, the response variable is observed time (in days), of which the total of failure times (death) is 119 and 58 observations were considered as censored (if the patient did not continued in the program for any reason or if the patient did not die until the end of the study).…”

Section: The Datamentioning

confidence: 99%

Using the Box-Cox family of distributions to model censored data

et al. 2022

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The study of the expected time until an event of interest is a recurring topic in different fields, such as medical, economics and engineering. The Kaplan-Meier method and the Cox proportional hazards model are the most used methodologies to deal with such kind of data. Nevertheless, in recent years, the generalised additive models for location, scale and shape (GAMLSS) models – which can be seen as distributional regression and/or beyond the mean regression models – have been standing out as a result of its highly flexibility and ability to fit complex data. GAMLSS are a class of semi-parametric regression models, in the sense that they assume a distribution for the response variable, and any and all of its parameters can be modelled as linear and/or non-linear functions of a set of explanatory variables. In this paper, we present the Box-Cox family of distributions under the distributional regression framework as a solid alternative to model censored data.

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Section: The Datamentioning

confidence: 99%

Using the Box-Cox family of distributions to model censored data

et al. 2022

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“…This methodology is already implemented in the gamlss package [26] in R [20] that includes several continuous and discrete distributions with up to four parameters (conveniently denoted by µ,σ , ν and τ ), including a few highly skewed and kurtotic ones. This framework is being widely used in many different fields, such as economics [23], natural sciences [30] and medical field [21], among others.…”

Section: Gamlss Frameworkmentioning

confidence: 99%

A new continuous distribution on the unit interval applied to modelling the points ratio of football teams

Nakamura

Cerqueira

Ramires

et al. 2018

Journal of Applied Statistics

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We introduce a new flexible distribution to deal with variables on the unit interval based on a transformation of the sinh-arcsinh distribution, which accommodates different degrees of skewness and kurtosis and becomes an interesting alternative to model this type of data. We also include this new distribution into the generalised additive models for location, scale and shape (GAMLSS) framework in order to develop and fit its regression model. For different parameter settings, some simulations are performed to investigate the behaviour of the estimators. The potentiality of the new regression model is illustrated by means of a real dataset related to the points rate of football teams at the end of a championship from the four most important leagues in the world: Barclays Premier League (England), Bundesliga (Germany), Serie A (Italy) and BBVA league (Spain) during three seasons (

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

Cited by 2 publications

References 16 publications

Using the Box-Cox family of distributions to model censored data

Using the Box-Cox family of distributions to model censored data

A new continuous distribution on the unit interval applied to modelling the points ratio of football teams

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