I'm working on a Difference-in-Difference model. Unfortunately, I have very little experience with these models. I've visually inspected the data as a test for the parallel trend assumption, I believe it holds. However, as this is an analysis that will be peer-reviewed, I'm sure they will require an empirical test.

The visual inspection was done by plotting the means and fitted values for the entire time period 2010-2017, before the intervention (<2014), and after the intervention (>=2014).

I have individual level data for >30,000 subjects. I'm running unadjusted and adjusted DID models utilizing the "diff" command.

Code:
by YEAR_OF_DIAGNOSIS expand, sort: egen pc_uninsured_expand = mean(100*uninsured)
    
*Fitted
twoway (lfit pc_uninsured_expand YEAR_OF_DIAGNOSIS if expand==1) ///
    (scatter pc_uninsured_expand YEAR_OF_DIAGNOSIS if expand==1) ///
    (lfit pc_uninsured_expand YEAR_OF_DIAGNOSIS if expand==0) ///
    (scatter pc_uninsured_expand YEAR_OF_DIAGNOSIS if expand==0), ///
    ylabel (0(5)20) ytitle(Uninsured (%)) ///
    legend(label(1 "Expansion - Fitted") label(2 "Expansion") label(3 "Nonexpansion fitted") label(4 "Nonexpansion")) xlabel(#8)

1. Plotting of the data over entire time period.
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2. Plot of data before implementation of "treatment"
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3. Plot of data after implementation of "treatment"

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The differences in differences model that I will be running is the following; where the outcome is "uninsured" (1=uninsured, 0=insured), "expand" is my treatment variable (treated=1, not treated=0), and "exp_year" is the grouping variable for before/after 2014 (before=0, after=1).

Code:
diff uninsured, t(expand) p(exp_year) 

diff uninsured, t(expand) p(exp_year) cov(AGE SEX race_cat hispanic_cat NO_HSD_QUAR_16 MED_INC_QUAR_16)

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I'm looking for a simple way of "proving" that the parallel trend assumption holds.

Thanks!