title: A SAS macro for estimating transition probabilities in semiparametric models for recurrent events.
abstract: In many clinical studies involving event history analysis, the event of interest is non-fatal and may occur more than once for each subject. Models based on the theory of counting processes have been developed to deal with such data, the recurrences being considered as transitions in a Markovian process. Under this setting, the experimental units can move between states over time, and it is possible to estimate the corresponding transition probabilities employing regression models that incorporate the influence of covariates. Despite of this, most of the softwares are concerned only in the estimation of regression parameters and do not provide transition probabilities estimates. The aim of this paper is to present a SAS macro developed to estimate the transition probabilities, considering three approaches for the regression modeling. The macro is flexible enough to allow the user to select the model to be fit providing, for a given set of covariates, plots of the estimates for the predicted transition probabilities as a function of time.
authors.affiliation: Laboratory of Epidemiology and Statistics, Institute Dante Pazzanese of Cardiology, São Paulo, Brazil. apaes@ime.usp.br
authors.forename: Angela Tavares
authors.initials: AT
authors.lastname: Paes
authors.forename: Antonio Carlos Pedroso
authors.initials: AC
authors.lastname: de Lima
mesh_terms: D001938:Brazil; D002657:Child Development; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D007676:Kidney Failure, Chronic / Q000503:physiopathology; D008297:Male; D015233:Models, Statistical; D011336:Probability; D012008:Recurrence; D012044:Regression Analysis