An important goal of environmental health research is to assess the health
risks posed by mixtures of multiple environmental exposures. In these mixtures
analyses, flexible models like Bayesian kernel machine regression and multiple
index models are appealing because they allow for arbitrary non-linear
exposure-outcome relationships. Cependant, this flexibility comes at the cost of
low power, particularly when exposures are highly correlated and the health
effects are weak, as is typical in environmental health studies. We propose an
adaptive index modelling strategy that borrows strength across exposures and
outcomes by exploiting similar mixture component weights and exposure-response
relationships. In the special case of distributed lag models, in which
exposures are measured repeatedly over time, we jointly encourage co-clustering
of lag profiles and exposure-response curves to more efficiently identify
critical windows of vulnerability and characterize important exposure effects.
We then extend the proposed approach to the multivariate index model setting
where the true index structure — the number of indices and their composition
— is unknown, and introduce variable importance measures to quantify component
contributions to mixture effects. Using time series data from the National
Morbidity, Mortality and Air Pollution Study, we demonstrate the proposed
methods by jointly modelling three mortality outcomes and two cumulative air
pollution measurements with a maximum lag of 14 days.

Cet article explore les excursions dans le temps et leurs implications.

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