qgcomp with mixed effects model, GEE

alexpkeil1 / qgcomp

QGcomp (quantile g-computation): estimating the effects of exposure mixtures. Works for continuous, binary, and right-censored survival outcomes. Flexible, unconstrained, fast and guided by modern causal inference principles

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qgcomp with mixed effects model, GEE #2

Open alexpkeil1 opened 5 years ago

alexpkeil1 commented 5 years ago

Would be nice to have a longitudinal qgcomp with some of the following possibilities:

Effects of increasing all prior exposures by one quantile
Quantiles for time-varying exposures that are based on single, common set of exposures OR quantiles that vary over time
Prompt effects with mixed effects models
Incorporation of arbitrary longitudinal models

alexpkeil1 commented 3 years ago

Note that a GEE-like approach to clustered data (e.g. longitudinal measurements on individuals) can be done using qgcomp.boot functions with the id parameter set to the cluster identifier. Like a GEE, the mean function (regression parameters) will be the the same as a GLM (in expectation for GEE, but exactly in the case of qgcomp). However, the bootstrap variance estimates will be clustering appropriate.

alexpkeil1 commented 1 year ago

Note this approach has now been implemented by Welch et al in https://doi.org/10.1016/j.envint.2021.106787

alexpkeil1 commented 1 year ago

There is also a gist with a brief example of this: https://gist.github.com/alexpkeil1/1887e2f87d92e682c517dd8fe74415b4

sahmoho commented 5 months ago

Hello, I was wondering if using qgcomp.glm.boot with the "id" specification with a paired time-varying exposure and paired time-varying outcome for each cluster was appropriate? Does this estimate the change in outcome driven by "unit change" in mixture quartiles between timepoints? Or is it better to use a difference measure for qgcomp that is not time-varying and use that to assess the impact of per quartile increase in mixture concentration change on outcome between timepoints?