Hi everyone,
I am seeking to correct my Monte Carlo-derived CI's for indirect paths in lavaan for multiple comparisons.
I am attempting to stay away from applying a Bonferroni-centered solution. This would imply changing my alpha level and thus the level of my CI, but given that I have to correct for over a hundred tests this becomes too stringent. Therefore I am seeking to apply an FDR-analogue solution.
A proposed solution for a similar problem (
link to source) is to control the CI's for the f
alse coverage rate. But this solution requires me to undergo parameter selection first, by for instance, thresholding the p-values associated with said parameters.
The problem is, as far as I understand, I cannot take this 1st step of selecting a number of parameters for whom to compute the CI's as the p-values associated with indirect effects in lavaan are non-informative (due to the sampling distribution associated with indirect parameters being non-normal). Which is why I use the Monte Carlo method to get my CI's in the first place.
I wonder, the following:
- If the False Coverage Rate solution is actually valid in this context
- If there is an alternative to using p-values for selecting the parameters that I will then compute FCR-corrected CI's for. I thought of making my selection equal to my number of parameters, but mathematically this seems to lead to the same alpha level. See equation in Algorithm 2.1 in source [1].
- If there is any other method out there for dealing with this issue.
References to the False Coverage Rate solution can be found here:
[2] Benjamini, Y., & Yekutieli, D. (2005). False Discovery Rate–Adjusted Multiple Confidence Intervals for Selected Parameters.
Journal of the American Statistical Association,
100(469), 71–81.
https://doi.org/10.1198/016214504000001907
Thank you.
Sofia