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Multivariate statistical analysis for anatomic pathology. Part II: failure time analysis

R T Vollmer1

  • 1Department of Laboratory Medicine, VA Medical Center, Durham, North Carolina 27705, USA.

American Journal of Clinical Pathology
|October 1, 1996
PubMed
Summary
This summary is machine-generated.

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This review covers survival analysis methods for understanding time to failure, focusing on how prognostic factors influence outcomes. It highlights the Cox proportional hazards model and alternative approaches using breast cancer data.

Area of Science:

  • Biostatistics
  • Medical Statistics
  • Epidemiology

Background:

  • Survival analysis is crucial for determining time to event outcomes.
  • Prognostic factors significantly influence patient survival.
  • Accurate statistical modeling is essential in clinical research.

Purpose of the Study:

  • To review methods for survival analysis.
  • To discuss the Cox proportional hazards model and its limitations.
  • To present alternative survival analysis models.

Main Methods:

  • Review of statistical methodologies for survival analysis.
  • Presentation of the Cox proportional hazards model.
  • Examination of residual analysis for model diagnostics.
  • Brief introduction to alternative survival models.

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Main Results:

  • The Cox proportional hazards model is a widely used method for survival analysis.
  • Residual analysis can identify issues with Cox model assumptions.
  • Alternative models offer different approaches to survival data analysis.

Conclusions:

  • Survival analysis is a key tool in medical research.
  • Understanding and applying appropriate models, like the Cox model, is vital.
  • Model diagnostics are important for reliable results.