Model selection for genetic and epidemiological data [back]
The afternoon on model choice at the London School of Hygiene (!) and Tropical Medicine was worth the short trip from Paris, especially when the weather in London felt like real summer: walking in the streets was a real treat! The talks were also interesting in that the emphasis was off-key from my usual statistics talks and thus required more focus from me. The first talk by Stijn Vansteelandt emphasized (very nicely) the role of confounders and exposure in causal inference in ways that were novel to me (although it seems in the end that a proper graphical modelling of all quantities involved in the process would allow for a standard statistical analysis). I also had troubles envisioning the Bayesian version of the approach, although Stijn referred to a recent paper by Wang et al. While Stijn has a joint paper in the Series B that just arrived on my desk, this talk is more related to appear in Statistical Methodology in Medical Research (The second talk was mine and presumably too technical in that I should have gotten rid of the new mathematical assumptions [A1]-[A4] altogether.) The third was a fascinating statistical analysis by Doug Speed of an important genetic heritability paper, by Yang et al., where he took the assumptions of the model one at a time to see how they were impacting the conclusions and found that none was to blame. The fourth and final talk by David Clayton covered the role of link functions in GLMs applied to epidemiological models, in connection with older papers from the 1990′s, to conclude that the choice of the link function mattered for the statistical properties of the variable selection procedures, which I found a bit puzzling based on my (limited) econometric intuition that all link functions lead to consistent pseudo-models. In any case, this was a fairly valuable meeting, furthermore attended by a very large audience.