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Defining causal mediation with a longitudinal mediator and a survival outcome. (English) Zbl 1436.62018

Summary: In the context of causal mediation analysis, prevailing notions of direct and indirect effects are based on nested counterfactuals. These can be problematic regarding interpretation and identifiability especially when the mediator is a time-dependent process and the outcome is survival or, more generally, a time-to-event outcome. We propose and discuss an alternative definition of mediated effects that does not suffer from these problems, and is more transparent than the current alternatives. Our proposal is based on the extended graphical approach of M. J. Robins and T. S. Richardson [“Alternative graphical causal models and the identification of direct effects”, in: Shrout (ed.), Causality and psychopathology: finding the determinants of disorders and their cures. Oxford: Oxford University Press (2011)], where treatment is decomposed into different components, or aspects, along different causal paths corresponding to real world mechanisms. This is an interesting alternative motivation for any causal mediation setting, but especially for survival outcomes. We give assumptions allowing identifiability of such alternative mediated effects leading to the familiar mediation g-formula [J. Robins, Math. Modelling 7, 1393–1512 (1986; Zbl 0614.62136)]; this implies that a number of available methods of estimation can be applied.

MSC:

62A01 Foundations and philosophical topics in statistics
62C05 General considerations in statistical decision theory
62H22 Probabilistic graphical models
62P10 Applications of statistics to biology and medical sciences; meta analysis
62N01 Censored data models

Citations:

Zbl 0614.62136

Software:

invGauss
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References:

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