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Problems and prediction in survival-data analysis

R Henderson1

  • 1Department of Mathematics and Statistics, University of Newcastle upon Tyne, U.K.

Statistics in Medicine
|January 30, 1995
PubMed
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Cox's proportional hazards model remains influential in survival analysis. This review examines recent developments and challenges, particularly in predictive inference accuracy compared to expert judgment.

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • David Cox's 1972 paper on 'Regression models and life tables' is highly cited.
  • The proportional hazards model and partial-likelihood technique are widely applied in scientific and medical research.
  • Concerns exist regarding the appropriate application and relevance of theoretical studies based on these methods.

Purpose of the Study:

  • To review recent developments in Cox's proportional hazards model.
  • To discuss ongoing challenges, with a focus on predictive inference.
  • To investigate the accuracy of model-based predictions versus expert judgment.

Main Methods:

  • Review of recent literature on proportional hazards models.
  • Discussion of theoretical and applied challenges.

Related Experiment Videos

  • An illustrative example comparing model predictions with consultant judgments.

    • Main Results:

    • The proportional hazards model and partial-likelihood technique have seen extensive application.
    • Recent developments and continuing problems, especially in predictive inference, are highlighted.
    • An example demonstrates the investigation into predictive accuracy.

    Conclusions:

    • Cox's proportional hazards model remains a cornerstone in survival data analysis.
    • Critical evaluation of its application and predictive capabilities is essential.
    • Further research is needed to refine predictive inference and its comparison with expert judgment.