*! version 1.2.0 15apr2019

Title

pdc -- Proportion of Days Covered (of a medication)

Syntax

pdc fill_date days_supply [if] [in]
, [ id(str) drug(str) start(str) end(str) length(#) credit ]

In the syntax, fill_date is the date when the prescription was filled and days_supply is the number of days that the medication was intended to last.

id(string) specifies the patient identifier if the data contain multiple patients

drug(string) specifies the drug variable if the data contain multiple medications

start(string) specifies a study start-date and must be specified in the DDmonCCYY format, such as start(01jan2013); default is to use the first fill_date in the data

end(string) specifies a study end-date, such as end(31dec2018); default is to use the date corresponding to the last (fill_date + days_supply) - 1

length(#) specifies a study duration as an alternative to specifying end(). Either length() or end() can be specified, but not both

credit specifies that overlapping refill periods should be spaced out, thereby giving "credit" to the patient for the overlapping supply of medication

Description

The Proportion of days covered (PDC) is a widely-used measure of adherence (also referred to as compliance) to a medication regimen (as an alternative to the medication
Possession Ratio (MPR) which can be implemented using mpr). PDC utilizes administrative pharmacy claims data to assess how frequently patients refill their prescriptions,
and assumes that patients who refill their prescriptions regularly also consume the medications as prescribed.
The general formula for PDC is:

days of supply on hand in the study period
-------------------------------------------
number of days in the study period

In this equation, the numerator indicates the actual count of days in which the patient has medication available (each day is categorized as either 1 or 0, representing
having or not having medication available on that day). Many investigators prefer the PDC over MPR
because it does not double count overlapping refills (which is due to refilling the prescription prior to running out of the drug on hand).
Consequently, the PDC equation can never exceed 1.0, whereas the MPR may exceed 1.0 if the numerator (e.g. total pill count) is greater than the number of days
in the study period.

pdc computes the proportion of days covered under a large number of scenarios. The PDC can be computed for a single patient or multiple patients,
with prescriptions filled for one medication or multiple medications. Moreover, PDC provides the user with tremendous flexibility in specifying the study period.
The two most common methods for setting the study period are; (1) a fixed study period, which keeps the denominator constant at a fixed time
interval (e.g. by specifying start() and end(), or by specifying start() and length()) and (2), a variable study period,
which allows the number of days in the denominator to vary, depending on how the user specifies either the start() or end() of the study period.
For example, specifying start() but not end() will result in the total days of supply in the last refill to be counted in the PDC (while the first
fill may be truncated for the days of supply spanning earlier than the study start date). Conversely, specifying end() but not start()
will result in the total amount of supply in the first prescription to be counted in the PDC (while the last refill may be truncated if the days supply of refill
spans beyond the observation end date).

Options

id(string) the patient identifier if the data contain multiple patients. When id() is not specified, PDC assumes that there is
a single patient in the data.

drug(string) the variable specifying the drug if the data contain multiple medications per patient. When drug() is not specified, PDC assumes that there is
only one medication per patient in the data.

start(string) represents the desired start date of a study period, aand must be specified in the DDmonCCYY format (e.g. 01feb2018). When start()
is not specified, pdc uses the first fill_date.

end(string) represents the desired end date of a study period, and must be specified in the DDmonCCYY format (e.g. 31dec2018). When end()
is not specified, pdc uses the date corresponding to the last (fill_date + days_supply) - 1.

length(#) specifies a study duration as an alternative to specifying end(). Either length() or end() can be specified, but not both.

credit specifies that overlapping refill periods should be spaced out by moving the next fill_date forward to coincide with day after the previous refill is exhausted. This
essentially gives the patient "credit" for having more supply of medication on hand.

Examples

There are four possible scenarios in which pdc can be implemented: 1) a
single patient on a single medication, 2) a single patient on multiple medications,
3) multiple patients on a single medication, and 4) multiple patients on multiple medications. The
examples below are described accordingly:

1) A single patient on a single medication

setup

. use medadheredata.dta, clear

. keep if id==1 & drug==1

If the data are for a single patient and single medication, we do not specify either id() or drug().
Here we specify the start and end dates of the study period to span all days in 2013.

. pdc fill_date days_supply, start(01jan2013) end(31dec2013)

2) A single patient on multiple medications

setup

. use medadheredata.dta, clear

. keep if id==1

If the data are for a single patient on multiple medications, we do specify drug() but not id().
Here we specify the start date and length of the study period to span 365 days in 2013 (as an alternative to specifying end()).

. pdc fill_date days_supply, drug(drug) start(01jan2013) length(365)

3) Multiple patients on a single drug

setup

. use medadheredata.dta, clear

. keep if drug==1

If the data are for multiple patients and a single medication, we specify id() but not drug().
Here we do not specify the start date, which means that pdc will use the earliest filldate as the start date. We set the end date of the study period
at 30June2013 (allowing each patient to have a different observation duration).

. pdc fill_date days_supply, id(id) end(30jun2013)

4) Multiple patients on multiple medications

setup

. use medadheredata.dta, clear

If the data are for multiple patients and multiple medications, we specify both id() and drug().
Here we do not specify either the start date or the end date, but specify the study period as being 180 days. Additionally, we specify credit
to move overlapping refill periods forward.

. pdc fill_date days_supply, id(id) drug(drug) length(180) credit

If there is a sufficient sample size, we could also look at confidence intervals.

. mean pdc, over(drug)

or alternatively

. ratio supply study_days, over(drug)

Some investigators prefer to dichotomize the adherence rate at a cut-point at 0.80, in
which patients above the cut-point are considered adherent and those below the cut-point are considered non-adherent.
Here we generate a binary variable and then tabulate by drug.

. generate pdc80 = cond(pdc >= .80,1,0)

. tabulate pdc80 drug, column

References

Andrade, S. E., Kahler, K. H., Frech, F., and Chan, K. A. (2006). Methods for evaluation of medication adherence and persistence using automated databases.
Pharmacoepidemiology and drug safety 15: 565-574.

Cramer, J. A., Roy, A., Burrell, A., Fairchild, C. J., Fuldeore, M. J., Ollendorf, D. A., & Wong, P. K. (2008).
Medication compliance and persistence: terminology and definitions. Value in Health 11: 44-47.

Farmer, K. C. (1999). Methods for measuring and monitoring medication regimen adherence in clinical trials and clinical practice.
Clinical Therapeutics 21: 1074-1090.

Hess, L. M., Raebel, M. A., Conner, D. A. and Malone, D. C. (2006). Measurement of adherence in pharmacy administrative databases: a proposal
for standard definitions and preferred measures. Annals of Pharmacotherapy 40: 1280-1288.

Peterson, A. M., Nau, D. P., Cramer, J. A., Benner, J., Gwadry-Sridhar, F. and Nichol, M. (2007).
A checklist for medication compliance and persistence studies using retrospective databases. Value in Health 10: 3-12.

Steiner, J. F., Koepsell, T. D., Fihn, S. D., & Inui, T. S. (1988). A general method of compliance assessment
using centralized pharmacy records: description and validation. Medical care 26: 814-823.

Citation of pdc

pdc is not an official Stata command. It is a free contribution
to the research community, like a paper. Please cite it as such:

Linden, Ariel (2018). PDC: Stata module for computing the Proportion of Days Covered of a medication.
http://econpapers.repec.org/software/bocbocode/s458551.htm

Author

Ariel Linden

President, Linden Consulting Group, LLC

alinden@lindenconsulting.org

http://www.lindenconsulting.org

Also see

Online: mpr (if installed)