Calculating treatment adherence/persistence from individual level drug data

ha33 — Thu, 09 Nov 2023 11:59:31 GMT

Hi everyone,

I have some individual-level prescription redemption data and I would like to calculate the point prevalence of drug use, defined as the percentage of the population being treated at monthly intervals one year before and after to the initiation of an intervention (intervention starts at different time points for each individual, but every individual has 1 year-follow-up in both directions).

My current data looks something like this:

Table 1

id	drug_atc	date_of_redemption	grace_period	days_supply	last_day_of_supply	intervention_date	cov_by_last	intervention
1	AA10A	23/12/2020	30	30	21/02/2021	01/02/2021	-	0
1	AA10A	31/01/2021	30	30	01/04/2021	01/02/2021	1	0
1	AA10A	04/02/2021	30	30	05/04/2021	01/02/2021	1	1
1	AA10A	07/03/2021	30	30	06/05/2021	01/02/2021	1	1
1	AA10A	15/05/2021	30	30	14/07/2021	01/02/2021	0	1
2	AA10A	02/04/2021	30	60	01/07/2021	01/06/2021	-	0
2	AA10A	05/08/2021	30	60	03/11/2021	01/06/2021	0	1
2	AA10A	06/09/2021	30	30	05/11/2021	01/06/2021	1	1

Last_day_of_supply is the days_supply + grace_period.

Cov_by_last denotes if the prescription was filled before the last_day_of_supply of the previous prescription (yes=1, no=0).

Intervention denotes if the prescription was filled before (0) or after (1) the intervention.

I also have a shell table with each id, the date of intervention and date of the monthly intervals since intervention:

Table 2

ID	drug_atc	month_since_int	intervention_date	intervention	date_since_inv
1	AA10A	-12	01/02/2021	0	01/02/2020
1	AA10A	-11	01/02/2021	0	01/03/2020
1	AA10A	-10	01/02/2021	0	01/04/2020
1	AA10A	-9	01/02/2021	0	01/05/2020
1	AA10A	-8	01/02/2021	0	01/06/2020
1	AA10A	-7	01/02/2021	0	01/07/2020
1	AA10A	-6	01/02/2021	0	01/08/2020
1	AA10A	-5	01/02/2021	0	01/09/2020
1	AA10A	-4	01/02/2021	0	01/10/2020
1	AA10A	-3	01/02/2021	0	01/11/2020
1	AA10A	-2	01/02/2021	0	01/12/2020
1	AA10A	-1	01/02/2021	0	01/01/2021
1	AA10A	0	01/02/2021	1	01/02/2021
1	AA10A	1	01/02/2021	1	01/03/2021
1	AA10A	2	01/02/2021	1	01/04/2021
1	AA10A	3	01/02/2021	1	01/05/2021
1	AA10A	4	01/02/2021	1	01/06/2021
1	AA10A	5	01/02/2021	1	01/07/2021
1	AA10A	6	01/02/2021	1	01/08/2021
1	AA10A	7	01/02/2021	1	01/09/2021
1	AA10A	8	01/02/2021	1	01/10/2021
1	AA10A	9	01/02/2021	1	01/11/2021
1	AA10A	10	01/02/2021	1	01/12/2021
1	AA10A	11	01/02/2021	1	01/01/2022
1	AA10A	12	01/02/2021	1	01/02/2022
2	AA10A	-12	01/06/2021	0	01/06/2020
2	AA10A	-11	01/06/2021	0	01/07/2020
2	AA10A	-10	01/06/2021	0	01/08/2020

I would like to create a table like this,

table 3

ID	drug_atc	month_since_int	in_treatment	intervention
1	AA10A	-12	1	0
1	AA10A	-11	1	0
1	AA10A	-10	1	0
1	AA10A	-9	1	0
1	AA10A	-8	1	0
1	AA10A	-7	1	0
1	AA10A	-6	1	0
1	AA10A	-5	1	0
1	AA10A	-4	1	0
1	AA10A	-3	1	0
1	AA10A	-2	1	0
1	AA10A	-1	1	0
1	AA10A	0	1	1
1	AA10A	1	1	1
1	AA10A	2	1	1
1	AA10A	3	0	1
1	AA10A	4	1	1
1	AA10A	5	0	1
1	AA10A	6	1	1
1	AA10A	7	0	1
1	AA10A	8	1	1
1	AA10A	9	0	1
1	AA10A	10	1	1
1	AA10A	11	0	1
1	AA10A	12	0	1
2	AA10A	-12	1	0
2	AA10A	-11	1	0
2	AA10A	-10	0	0

where in_treatment denotes whether the individual was covered by a days_supply of the drug on the day of the start of the month_since_int.

Eventually, the final table should look something like this, which contains the percentage of individuals in treatment at each month in relation to the intervention.

month_since_int	drug_atc	in_treat_percent
-12	AA10A	70.0%
-11	AA10A	60.0%
-10	AA10A	80.0%
-9	AA10A	70.0%
-8	AA10A	60.0%
-7	AA10A	60.0%
-6	AA10A	70.0%
-5	AA10A	70.0%
-4	AA10A	60.0%
-3	AA10A	80.0%
-2	AA10A	60.0%
-1	AA10A	70.0%
0	AA10A	60.0%
1	AA10A	60.0%
2	AA10A	50.0%
3	AA10A	40.0%
4	AA10A	50.0%
5	AA10A	30.0%
6	AA10A	30.0%
7	AA10A	20.0%
8	AA10A	20.0%
9	AA10A	30.0%
10	AA10A	20.0%

The key step I am struggling with is creating table 3, but I happy to receive input to any of the steps, it does not have to be the total solution.

Thanks

topic Calculating treatment adherence/persistence from individual level drug data in SAS Programming

Calculating treatment adherence/persistence from individual level drug data