Simpson's Paradox in Survival Models

Marco Scarsini, Yosef Rinott and Clelia Di Serio

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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.

Keywords: Cox model; detrimental covariate; linear transformation model; omitting covariates; positive dependence; proportional hazard; proportional odds model; protective covariate; total positivity (search for similar items in EconPapers)
Date: 2009-09-01
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Citations: View citations in EconPapers (1)

Published in Scandinavian Journal of Statistics, 2009, Vol.36,n°3, pp.463-480. ⟨10.1111/j.1467-9469.2008.00637.x⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00464530

DOI: 10.1111/j.1467-9469.2008.00637.x

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