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.
Autori: | |
Autori: | Brambilla, C; Rossi, C; Schinaia, G |
Titolo: | Tree-structured analysis of survival data - Search for latent diagnostic factors in a tumour study |
Nome del convegno: | 7th International Symposium on Applied Stochastic Models and Data Analysis |
Luogo del convegno: | DUBLIN, IRELAND |
Anno del convegno: | JUN 12-15, 1995 |
Rilevanza: | Rilevanza internazionale |
Sezione: | contributo |
Data di pubblicazione: | 1997 |
Settore Scientifico Disciplinare: | Settore MED/01 - Statistica Medica |
Lingua: | English |
Tipologia: | Intervento a convegno |
Citazione: | 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. |
Appare nelle tipologie: | 02 - Intervento a convegno |