Hi,

I would like to perform an interrupted-time-series analysis to assess the impact of a policy on medicine sales.

Products/medicines are divided into 2 categories (A and B). The policy is intended to have two effects: i) decrease the overall sales; and ii) increase the relative sales of B vs A (eg, if B currently accounts for 50% of total sales, the policy intends to increase this to 60%). The impact is expected to be gradual and/or lagged.

The data is set up as a panel with quarterly sales (2012-2017) of ~50 products. This data is available for 5 countries. The policy was introduced at the same time for all countries/products (Q3 2014). The outcome variable is sales units per population (rate). The data example below shows how the data is set up (dummy/incomplete data).

Does anyone have any advice on the best way to set this model up? I've tried using the user-written package itsa (ssc install itsa), but have come unstuck estimating the differing effects on category A and B products. I had also considered a poisson model for the count of units sold, offset for population.

Thank you for any advice you can provide.


Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float(country year quarter t product) str1 category float(policy units population rate)
1 2014 1 1 1 "A" 0 100 1000        .1
1 2014 2 2 1 "A" 0 105 1000      .105
1 2014 3 3 1 "A" 1 120 1000       .12
1 2014 4 4 1 "A" 1 125 1000      .125
1 2015 1 5 1 "A" 1 130 1000       .13
1 2015 2 6 1 "A" 1 130 1000       .13
1 2014 1 1 2 "B" 0  60 1000       .06
1 2014 2 2 2 "B" 0  60 1000       .06
1 2014 3 3 2 "B" 1  40 1000       .04
1 2014 4 4 2 "B" 1  35 1000      .035
1 2015 1 5 2 "B" 1  32 1000      .032
1 2015 2 6 2 "B" 1  30 1000       .03
1 2014 1 1 3 "A" 0 125 1000      .125
1 2014 2 2 3 "A" 0 120 1000       .12
1 2014 3 3 3 "A" 1 145 1000      .145
1 2014 4 4 3 "A" 1 150 1000       .15
1 2015 1 5 3 "A" 1 152 1000      .152
1 2015 2 6 3 "A" 1 160 1000       .16
2 2014 1 1 1 "A" 0  40  750 .05333333
2 2014 2 2 1 "A" 0  35  750 .04666667
2 2014 3 3 1 "A" 1  45  750       .06
2 2014 4 4 1 "A" 1  50  750 .06666667
2 2015 1 5 1 "A" 1  51  750      .068
2 2015 2 6 1 "A" 1  50  750 .06666667
2 2014 1 1 2 "B" 0  40  750 .05333333
2 2014 2 2 2 "B" 0  40  750 .05333333
2 2014 3 3 2 "B" 1  40  750 .05333333
2 2014 4 4 2 "B" 1  35  750 .04666667
2 2015 1 5 2 "B" 1  37  750 .04933333
2 2015 2 6 2 "B" 1  35  750 .04666667
2 2014 1 1 3 "A" 0  60  750       .08
2 2014 2 2 3 "A" 0  65  750 .08666667
2 2014 3 3 3 "A" 1  80  750 .10666667
2 2014 4 4 3 "A" 1  85  750 .11333334
2 2015 1 5 3 "A" 1  87  750      .116
2 2015 2 6 3 "A" 1  90  750       .12
end