Prognostic factors: Rationale and methods of analysis and integration

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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)105-112
Number of pages8
JournalBreast Cancer Research and Treatment
Issue number1
Publication statusPublished - Jan 1994


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

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


Dive into the research topics of 'Prognostic factors: Rationale and methods of analysis and integration'. Together they form a unique fingerprint.

Cite this