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.
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Clark, G.M., Hilsenbeck, S.G., Ravdin, P.M. et al. Prognostic factors: Rationale and methods of analysis and integration. Breast Cancer Res Tr 32, 105–112 (1994). https://doi.org/10.1007/BF00666211
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DOI: https://doi.org/10.1007/BF00666211