Lifetime Data Anal - On collapsibility and confounding bias in Cox and Aalen regression models.

Tópicos

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Resumo

We study the situation where it is of interest to estimate the effect of an exposure variable [Formula: see text] on a survival time response [Formula: see text] in the presence of confounding by measured variables [Formula: see text]. Quantifying the amount of confounding is complicated by the non-collapsibility or non-linearity of typical effect measures in survival analysis: survival analyses with or without adjustment for [Formula: see text] typically infer different effect estimands of a different magnitude, even when [Formula: see text] is not associated with the exposure, and henceforth not a confounder of the association between exposure and survival time. We show that, interestingly, the exposure coefficient indexing the Aalen additive hazards model is not subject to such non-collapsibility, unlike the corresponding coefficient indexing the Cox model, so that simple measures of the amount of confounding bias are obtainable for the Aalen hazards model, but not for the Cox model. We argue that various other desirable properties can be ascribed to the Aalen model as a result of this collapsibility. This work generalizes recent work by Janes et al. (Biostatistics 11:572-582, 2010).

Resumo Limpo

studi situat interest estim effect exposur variabl formula see text surviv time respons formula see text presenc confound measur variabl formula see text quantifi amount confound complic noncollaps nonlinear typic effect measur surviv analysi surviv analys without adjust formula see text typic infer differ effect estimand differ magnitud even formula see text associ exposur henceforth confound associ exposur surviv time show interest exposur coeffici index aalen addit hazard model subject noncollaps unlik correspond coeffici index cox model simpl measur amount confound bias obtain aalen hazard model cox model argu various desir properti can ascrib aalen model result collaps work general recent work jane et al biostatist

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