Abstract
This article aims to provide rigorous and convenient statistical models for dealing with high-variability phenomena. The presence of discrepance in variance represents a substantial issue when it is not possible to reduce variability before analysing the data, leading to the possibility to estimate an inadequate model. In this paper, the application of Generalized Additive Model for Location, Scale and Shape (GAMLSS) and the use of finite mixture model for GAMLSS will be proposed as a solution to the problem of overdispersion. An application to Liver fibrosis data is illustrated in order to identify potential risk factors for patients, which could determine the presence of the disease but also its levels of severity.
Highlights
In many applicative studies, the average level of certain response variables cannot be controlled unless variability in its measurements is previously reduced
The idea of using a GAMLSS approach for handling our problem comes from the idea of [5] consisting in the use of an EM maximum likelihood estimation algorithm [6] to deal with overdispersed generalized linear models (GLM)
GAMLSS could be widely applied to medical research, since high variability and overdispersion are frequently recurring situations when clinical data are analysed
Summary
The average level of certain response variables cannot be controlled unless variability in its measurements is previously reduced This happens very often in medical studies when the interest lies in determining the presence of some diseases and the relevant level of progression, through the use of some laboratory measurements. Those are characterized by high-variability between observations and no gold standard exists; or, alternatively, when repeated measurements with high-variability are available for the same patient. The use of a finite mixture model for GAMLSS will be proposed as a solution to the problem of multimodality in determining the presence of liver diseases and to establish its level of severity.
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