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Tree‐structured analysis of survival data—search for latent diagnostic factors in a tumour study

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  • Carla Brambilla
  • Carla Rossi
  • Giuseppe Schinaia

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. © 1998 John Wiley & Sons, Ltd.

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  • Carla Brambilla & Carla Rossi & Giuseppe Schinaia, 1997. "Tree‐structured analysis of survival data—search for latent diagnostic factors in a tumour study," Applied Stochastic Models and Data Analysis, John Wiley & Sons, vol. 13(3‐4), pages 333-343, September.
    • Handle: RePEc:wly:apsmda:v:13:y:1997:i:3-4:p:333-343
    DOI: 10.1002/(SICI)1099-0747(199709/12)13:3/43.0.CO;2-7
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