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Simpson's Paradox in Survival Models

Author

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  • Marco Scarsini

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique, Dipartimento di Scienze Economiche e Aziendali - LUISS - Libera Università Internazionale degli Studi Sociali Guido Carli [Roma])

  • Yosef Rinott
  • Clelia Di Serio

Abstract

In the context of survival analysis it is possible that increasing the value of a covariate X has a beneficial effect on a failure time, but this effect is reversed when conditioning on any possible value of another covariate Y. When studying causal effects and influence of covariates on a failure time, this state of affairs appears paradoxical and raises questions about the real effect of X. Situations of this kind may be seen as a version of Simpson's paradox. In this paper, we study this phenomenon in terms of the linear transformation model. The introduction of a time variable makes the paradox more interesting and intricate: it may hold conditionally on a certain survival time, i.e. on an event of the type {T>t} for some but not all t, and it may hold only for some range of survival times.

Suggested Citation

  • Marco Scarsini & Yosef Rinott & Clelia Di Serio, 2009. "Simpson's Paradox in Survival Models," Post-Print hal-00464530, HAL.
    • Handle: RePEc:hal:journl:hal-00464530
    DOI: 10.1111/j.1467-9469.2008.00637.x
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    Cited by:

    1. P. Vellaisamy, 2017. "Collapsibility of some association measures and survival models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(5), pages 1155-1176, October.

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