Breast Cancer Research and Treatment

, Volume 32, Issue 1, pp 105–112

Prognostic factors: Rationale and methods of analysis and integration

  • Gary M. Clark
  • Susan G. Hilsenbeck
  • Peter M. Ravdin
  • Michele De Laurentiis
  • C. Kent Osborne
Article

DOI: 10.1007/BF00666211

Cite this article as:
Clark, G.M., Hilsenbeck, S.G., Ravdin, P.M. et al. Breast Cancer Res Tr (1994) 32: 105. doi:10.1007/BF00666211
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Summary

With the proliferation of potential prognostic factors for breast cancer, it is becoming increasingly more difficult for physicians and patients to integrate the information provided by these factors into a single accurate prediction of clinical outcome. Here we review Cox's proportional hazards model, recursive partitioning, correspondence analysis, and neural networks for their respective capabilities in analyzing censored survival data in the presence of multiple prognostic factors, and we present some clinical applications where these models have been used.

Key words

correspondence analysis Cox model multivariate analysis neural networks prognostic factors recursive partitioning survival analysis 

Copyright information

© Kluwer Academic Publishers 1994

Authors and Affiliations

  • Gary M. Clark
    • 1
  • Susan G. Hilsenbeck
    • 1
  • Peter M. Ravdin
    • 1
  • Michele De Laurentiis
    • 2
  • C. Kent Osborne
    • 1
  1. 1.Division of Medical OncologyUniversity of Texas Health Science Center at San AntonioSan AntonioUSA
  2. 2.Cattedra di Oncologia Medica, Facoltà di MedicinaUniversità degli Studi “Federico II”NapoliItaly

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