Closed giulianonetto closed 3 months ago
Hi Giuliano! Unfortunately robust standard errors are not the same as addressing within-subject correlation arising from longitudinally collected measurements. Potentially you could go with
differentialTest(formula = ~ time_point*treatment_group + subject_ID, formula_null = ~ subject_ID...)
but this may involve a large number of predictors, which is not a good idea in general.
As of right now, we don't have any plans to implement more generalizations to corncob
, which I'm thinking of as in "essential maintenance only" mode. Instead, we are referring folks to our new package radEmu, which addresses some of the limitations of corncob -- most notably, differential detection of taxa, but we also have a option to address within-subject correlation in radEmu
. I recommend checking it out! Note that it's what we are using on all of our analysis these days 😻
That may not be what you want to hear, but I hope it at least answers your question.
Cool, I will check radEmu out! Thank you so much!!
Hi all!
Thank you so much for your awesome work!
Please, I have one question regarding experiments with repeated measures within each subject. I understand that in #63 there is a discussion about LRT for this type of situation, but I am not sure how that handles within-subject correlations.
In this setting, would it be appropriate to use
robust=TRUE
indifferentialTest
(or, in general,sand_vcov()
method)? Something of the generic form of the example below, testingi.e., would that be analogous to a GEE approach to longitudinal data, instead of what is mentioned in #63?
Thank you very much! Best, Giuliano