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Survival Trees: An Alternative Non-Parametric Multivariate Technique for Life History Analysis

Author

Listed:
  • Alessandra De Rose

    (ISTAT – Istituto Nazionale di Statistica)

  • Alessandro Pallara

    (ISTAT – Istituto Nazionale di Statistica)

Abstract

In this paper an extension of tree-structured methodology to cover censored survival analysis is discussed. Tree-based methods (also called recursive partitioning) provide a useful alternative to the classical survival data analysis techniques, such as the semi-parametric model of Cox, whenever the main purpose is defining groups of individuals, either with complete or censored life history, having different survival probability, based on the values of selected covariates. The essential feature of recursive partitioning is the construction of a decision rule in the form of a binary tree. Trees generally require fewer assumptions than classical methods and handle non standard and non linear data structures efficiently. Tree-growing methods make the processes of covariate selection and grouping of categories in event history models explicit. An example concerning the analysis of time to marriage of Italian women is presented.

Suggested Citation

  • Alessandra De Rose & Alessandro Pallara, 1997. "Survival Trees: An Alternative Non-Parametric Multivariate Technique for Life History Analysis," European Journal of Population, Springer;European Association for Population Studies, vol. 13(3), pages 223-241, September.
    • Handle: RePEc:spr:eurpop:v:13:y:1997:i:3:d:10.1023_a:1005844818027
    DOI: 10.1023/A:1005844818027
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    References listed on IDEAS

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    1. Ahn, Hongshik, 1996. "Log-normal regression modeling through recursive partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 21(4), pages 381-398, April.
    2. Ciampi, Antonio, 1991. "Generalized regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 12(1), pages 57-78, August.
    3. Loh, Wei-Yin, 1991. "Survival modeling through recursive stratification," Computational Statistics & Data Analysis, Elsevier, vol. 12(3), pages 295-313, November.
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    Cited by:

    1. Francesco C. Billari & Johannes Fürnkranz & Alexia Prskawetz, 2006. "Timing, Sequencing, and Quantum of Life Course Events: A Machine Learning Approach," European Journal of Population, Springer;European Association for Population Studies, vol. 22(1), pages 37-65, March.

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