A SAS macro for estimating transition probabilities in semiparametric models for recurrent events / A SAS macro for estimating transition probabilities in semiparametric models for recurrent events
Computer Methods and Programs in Biomedicine
; 75: 59-65, 2004.
Artigo
em Inglês
| Sec. Est. Saúde SP, SESSP-IDPCPROD, Sec. Est. Saúde SP
| ID: biblio-1062183
Biblioteca responsável:
BR79.1
Localização: BR79.1
RESUMO
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.
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Bases de dados nacionais
/
Brasil
Base de dados:
Sec. Est. Saúde SP
/
SESSP-IDPCPROD
Assunto principal:
Probabilidade
Tipo de estudo:
Estudo prognóstico
Idioma:
Inglês
Revista:
Computer Methods and Programs in Biomedicine
Ano de publicação:
2004
Tipo de documento:
Artigo
Instituição/País de afiliação:
Instituto Dante Pazzanese de Cardiologia/BR
/
Instituto Dante Pazzanese ed Cardiologia/BR