What is the best method of reporting multiple tests of equivalence? I am doing a study which will involve multiple tests of equivalence.  Is there a standard table for reporting such results? 
EDIT with more detail:
It is a longitudinal study with 5 time points.  Our hypothesis is that there is a change from baseline to 1 month and then stability after that. Initially, I thought of a multilevel model but there is no independent variable other than time. So I decided on t-tests between baseline and 1 month and TOST tests of equivalence between 1 and 3 months, 3 and 6, 6 and 9 and 9 and 12. 
There were also 5 areas tested.
So, I need a way to summarize all these tests in a table
 A: Regression table presentations are easy enough to modify to accommodate tests for equivalence, including relevance tests—where you base conclusions off of both tests for difference (tests of $H^{^{+}}_{0}$) and tests for equivalence (tests of $H^{^{-}}_{0}$). For example (assuming you are presenting multiple tests in a regression context, hence the $\beta$):

You can present both one-sided test statistics ($t_1$ and $t_2$) and their associated p-values ($p_1$ and $p_2$) from the tests for equivalence, and in addition present the test statistic $t$ and p-value ($p$) for the tests for difference.
In addition you may want to include a column for your definition of equivalence if it varies from test to test (I use $\Delta$ to indicate my equivalence/relevance threshold defined in units of my measures, and $\varepsilon$ to indicate this threshold defined in units of my test statistic). If you use a consistently defined of equivalence/relevance threshold for all tests, you would likely indicate that in a footnote to the table.
You can also facilitate interpretation by including columns to explicitly articulate rejection decisions for equivalence and difference tests. Including a relevance test column (combining results as I have illustrated here) may also facilitate interpretation.
Of course, one can also use this format to present independent tests, and to present different kinds of TOST test statistics (e.g. z test statistics like those used with non-parametric tests, exact binomial test statistics, etc.).
A: Others have given you more direct answers to your question, but I'm going to try and show a different solution to the problem. Unless I'm misunderstanding you, it seems like a two-way (period area) fixed effects model would work best (with robust SEs, of course!).
$$
y_i = \sum_k \alpha_k \times \mathbf{1}[i \in \text{period}_k] + \sum_j \beta_j \times \mathbf{1}[i \in \text{area}_j] + \epsilon_i
$$
You could do an F-test (joint null test) on all the $\alpha_k$ to see if there is any change across time at all, and you can do a Wald test to see if a subset of coefficients (in your case $\alpha_2 = \alpha_3 = \dots = \alpha_K$) are the same. This also gets around all of the multiple testing problems you should be worrying about if you're doing a bunch of pairwise tests.
All of the above can be done within the text. For a visual inspection.. I know you're asking for a table, but I think a plot like this with 95% intervals is much more convincing. But maybe it's just the style of my field.

