Logistic Model Case Study 1: Predicting Cause of Death

  • Frank E. HarrellJr.
Part of the Springer Series in Statistics book series (SSS)


Consider the randomized trial of estrogen for treatment of prostate cancer60 described in Chapter 8. In this trial, larger doses of estrogen reduced the effect of prostate cancer but at the cost of increased risk of cardiovascular death. Kay233 did a formal analysis of the competing risks for cancer, cardiovascular, and other deaths. It can also be quite informative to study how treatment and baseline variables relate to the cause of death for those patients who died.258 We subset the original dataset of those patients dying from prostate cancer (n = 130), heart or vascular disease (n = 96), or cerebrovascular disease (n = 31). Our goal is to predict cardiovascular death (cvd, n = 127) given the patient died from either cvd or prostate cancer. Of interest is whether the time to death has an effect on the cause of death, and whether the importance of certain variables depends on the time of death. We also need to formally test whether the data reductions and pretransformations in Chapter 8 are adequate for predicting cause of death.


Prostate Cancer Full Model Terminal Node Recursive Partitioning High Order Factor 
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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Frank E. HarrellJr.
    • 1
  1. 1.Department of BiostatisticsVanderbilt University School of MedicineNashvilleUSA

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