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Simpson's Paradox in Survival Models

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.
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Submitted on : Wednesday, March 17, 2010 - 1:49:43 PM
Last modification on : Thursday, January 11, 2018 - 6:19:31 AM

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Marco Scarsini, Yosef Rinott, Clelia Di Serio. Simpson's Paradox in Survival Models. Scandinavian Journal of Statistics, Wiley, 2009, Vol.36,n°3, pp.463-480. ⟨10.1111/j.1467-9469.2008.00637.x⟩. ⟨hal-00464530⟩

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