This paper presents a possible solution to the problem of identification of factors influencing long-term survival patients, using regression trees. The separation of the two classes of long-term survivors (cured patients) and of failed-to-cure patients is generalized to l* classes of survivors and is carried out via a latent variable, whose determinations are provided by the regression-tree classification. Two sets of factors are thus identified within the set of covariates: the factors influencing the prognosis and those influencing the survival classification (diagnostic factors). The relationship between the two sets is then explored, both theoretically and using an application to a data set of multiple myeloma patients. (C) 1998 John Wiley & Sons, Ltd.
Brambilla, C., Rossi, C., Schinaia, G. (1997). Tree-structured analysis of survival data - Search for latent diagnostic factors in a tumour study. In Applied Stochastic Models and Data Analysis (pp.333-343). W SUSSEX : JOHN WILEY & SONS LTD.
Tree-structured analysis of survival data - Search for latent diagnostic factors in a tumour study
ROSSI, CARLA;
1997-01-01
Abstract
This paper presents a possible solution to the problem of identification of factors influencing long-term survival patients, using regression trees. The separation of the two classes of long-term survivors (cured patients) and of failed-to-cure patients is generalized to l* classes of survivors and is carried out via a latent variable, whose determinations are provided by the regression-tree classification. Two sets of factors are thus identified within the set of covariates: the factors influencing the prognosis and those influencing the survival classification (diagnostic factors). The relationship between the two sets is then explored, both theoretically and using an application to a data set of multiple myeloma patients. (C) 1998 John Wiley & Sons, Ltd.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
