In searching for any info about marginal model and random-effects model, and how to choose between them, I have found some info but it was more-or-less mathematical abstract explanation (like for example here: https://stats.stackexchange.com/a/68753/38080). Somewhere I have found that there were observed substantial differences between a parameter estimates between these two methods/models (http://www.biomedcentral.com/1471-2288/2/15/), however the opposite was wrote by Zuur et al. (2009, p. 116; http://link.springer.com/book/10.1007%2F978-0-387-87458-6). Marginal model (generalized estimating equation approach) brings population-averaged parameters, while outputs from random-effects model (generalized linear mixed model) take into account random effect – subject (Verbeke et al. 2010, pp. 49–52; http://link.springer.com/chapter/10.1007/0-387-28980-1_16).
I would like to see some layman-like explanation of these models illustrated on some model (real-life) examples in language familiar to non-statistician and non-mathematician.
In detail, I would like to know:
When should be used marginal model and when should be used random-effects model? For which scientific questions are these models suitable?
How should be outputs from these models interpreted?