%0 Journal Article
%T Progresive diseases study using Markov¡äs multiple stage models
%A Juan Carlos Salazar
%A Ren¨¦ Iral Palomino
%A Esp estad¨ªstica
%J MedUNAB
%D 2005
%I Universidad Aut¨®noma de Bucaramanga
%X Risk factors and their degree of association with a progressive disease,such as Alzheimer¨ªs disease or liver cancer, can be identifi edby using epidemiological models; some examples of these modelsinclude logistic and Poisson regression, log-linear, linear regression,and mixed models. Using models that take into account not onlythe different health status that a person could experience betweenvisits but also his/her characteristics (i.e. age, gender, genetic traits,etc.) seems to be reasonable and justifi ed. In this paper we discussa methodology to estimate the effect of covariates that could beassociated with a disease when its progression or regression canbe idealized by means of a multi-state model that incorporates thelongitudinal nature of data. This method is based on the Markovproperty and it is illustrated using simulated data about Alzheimer¨ªsdisease. Finally, the merits and limitations of this method are discussed.
%K Alzheimer¡äs disease
%K genetic markers
%K multiple stage models
%K longuitudinal data
%K Markov¡äs dependence.
%U http://editorial.unab.edu.co/revistas/medunab/pdfs/r83_ar_c3.pdf