Examples Using Tree-Based Analysis

  • Heping Zhang
  • Burton H. Singer
Part of the Springer Series in Statistics book series (SSS, volume 0)


In epidemiologic studies, one of the most frequently encountered issues is to evaluate the association between a set of putative risk factors and a disease outcome, controlling for another set of potential confounders. In this chapter, we illustrate how to apply the tree-based method in this regard. The discussion is mostly adopted from Zhang and Bracken (1996).


Birth Control Linear Discriminant Analysis Spontaneous Abortion Terminal Node Adjusted Relative Risk 
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  1. [21]
    M.B. Bracken, K. Belanger, K.G. Hellenbrand, et al. Exposure to electromagnetic fields during pregnancy with emphasis on electrically heated beds: association with birthweight and intrauterine growth retardation. Epidemiology, 6:263–270, 1995.CrossRefGoogle Scholar
  2. [24]
    L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone. Classification and Regression Trees. Wadsworth, California, 1984.MATHGoogle Scholar
  3. [47]
    W.G. Cochran. Some methods of strengthening the common χ 2 test. Biometrics, 10:417–451, 1954.MATHCrossRefMathSciNetGoogle Scholar
  4. [89]
    E. Giovannucci, A. Ascherio, E.B. Rimm, M.J. Stampfer, G.A. Colditz, and W.C. Willett. Intake of carotenoids and retinol in relation to risk of prostate cancer. Journal of the National Cancer Institute, 87:1767–1776, 1995.CrossRefGoogle Scholar
  5. [143]
    N. Mantel and W. Haenszel. Statistical aspects of the analysis of data from retrospective studies of disease. Journal of the National Cancer Institute, 22:719–748, 1959.Google Scholar
  6. [152]
    O.S. Miettinen. Stratification by a multivariate confounder score. American Journal of Epidemiology, 104:609–620, 1976.Google Scholar
  7. [156]
    P.K. Mills, W.L. Beeson, R.L. Phillips, and G.E. Fraser. Bladder cancer in a low risk population: results from the Adventist Health Study. American Journal of Epidemiology, 133:230–239, 1991.Google Scholar
  8. [229]
    H.P. Zhang and M.B. Bracken. Tree-based, two-stage risk factor analysis for spontaneous abortion. American Journal of Epidemiology, 144:989–996, 1996.Google Scholar
  9. [10]
    A. Asuncion and D.J. Newman. UCI Machine Learning Repository. [], University of California, School of Information and Computer Sciences, Irvine, CA, 2007.

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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Epidemiology and Public HealthYale University School of MedicineNew HavenUSA
  2. 2.Emerging Pathogens InstituteUniversity of FloridaGainesvilleUSA

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