Valid to run post-hoc tests on matched data or running afoul of Table 2 Fallacy? After completing a difference-in-differences matched cohort analysis to estimate the pre/post treatment effect of a home health nursing intervention compared to a control group, my employers are interested in dissecting the matched data.
In particular, do we achieve different treatment effects looking at subsets of the original matched data (e.g., high vs. low risk scores, 0 vs. 1+ ER visits in the baseline time period)?
Is this a valid form of analysis or are we getting into trouble with the Table 2 Fallacy?

The Table 2 fallacy refers to when one interprets effect estimates of confounders in a regression table as-if they were the variable of interest all along. So no, the analysis you've described does not run afoul of this fallacy. That being said, you would need to be careful of multiple-test correction, and you would need to be aware that the data will very likely not be matched any more, so you should have a plan in place for how to model potential confounders.
