# Multiple testing correction

I use glm.nb from MASS package over many observations. I then extract the p-values for the interaction term. Due to multiple testing, I use BH method for correction for multiple testing, with FDR of 0.05. Sometimes, when I apply the negative binomial model to fit to the data, the model doesn't converge and ends up with a warning "Iteration limit reached" and has a huge theta value normally (> 10000). However, most of these times, the p-values obtained are quite small (< 10e-5). Of course these small values are wrong, as there was no convergence achieved. Now, I can find out these observations using the \$th.warn attribute which stores the warning message.

My question is, before performing multiple testing, should I remove these non-fit observations and therefore wrong p-values (as they might affect the BH process of correcting for FDR) or because I have already performed the test, removing these entries after finding the warning message would bias the FDR correction of the p-values and therefore I should just continue with FDR correction without removing these obviously wrong but small p-values???

Thank you!

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