AdhereR: Adherence to Medications

Alexandra L. Dima & Dan Dediu

2017-04-26

Estimating the adherence to medications from electronic healthcare data (EHD) has been central to research and clinical practice across clinical conditions. For example, large retrospective database studies may estimate the prevalence of non-adherence in specific patient groups and model its potential predictors and impact on health outcomes, while clinicians may have access to individual patient records that flag up possible non-adherence for further clinical investigation and intervention. Yet, adherence measurement is a matter of controversy. Many methodological studies show that the same data can generate different prevalence estimates under different parametrisations (Greevy et al., 2011; Gardarsdottir et al., 2010; Souverein et al., in press; Vollmer et al., 2012; Van Wijk et al., 2006).

These parametrisation choices are usually not transparently reported in published empirical studies, and adherence algorithms are either developed ad-hoc or for proprietary software. This lack of transparency and standardization has been one of the main methodological barriers against the development of a solid evidence base on adherence from EHD, and may lead to misinformed clinical decisions.

Here we describe AdhereR (version 0.1.0), an R package that aims to facilitate the computing of adherence from EHD, as well as the transparent reporting of the chosen calculations. It contains a set of R S3 classes and functions that compute, summarize and plot various estimates of adherence. A hypothetical dataset of medication events is included for demonstration and testing purposes. In this vignette, we start by defining the terms used in AdhereR. We then use medication records of two patients in the included dataset to illustrate the various decisions required and their impact on estimates, starting with the visualization of medication events, computation of persistence (treatment episode length), and computation of adherence (9 functions available in 3 versions: simple, per-treatment-episode, and sliding-window). Visualizations for each function are illustrated, and the interactive visualization function is presented.

While we tested the package relatively extensively, we cannot guarantee that bugs and errors do not exist, and we encourage the users to contact us with suggestions, bug reports, comments (or even just to share their experiences using the package) either by e-mail (to Dan ddediu@gmail.com or Alexandra alexadima@gmail.com) or using GitHub’s reporting mechanism at our repository https://github.com/ddediu/AdhereR, which contains the full source code of the package (including this vignette).

Definitions

Adherence to medications is described as a process consisting of 3 successive elements/stages: initiation, implementation, and discontinuation (Vrijens et al., 2012). After initiating treatment (first medication intake), a patient would continue with implementing the regimen for a time period, ideally equal to the recommended time necessary for achieving therapeutic benefits; the quality of implementation is commonly labelled adherence and broadly operationalized as a ratio of medication quantity used versus prescribed in a time period. If patients discontinue medication earlier than the recommended time period, the period before discontinuation is described as persistence, in contrast to the following period of non-persistence.

The ideal measurement of this process would record the prescription moment and every medication intake with an exact time-stamp. This would allow, for example, to describe adherence to a twice-daily medication prescribed for 1 year in maximum detail: how long was the delay between prescription and the moment of the first medication intake, whether each of the two prescribed administrations per day corresponded to an intake event and at what time, how much medication was taken versus prescribed on any time interval while the patient persisted with treatment, any specific implementation patterns (e.g. missing or delaying the first daily dose), and when exactly the last medication intake event took place during that year. While this level of detail can be obtained by careful use of electronic monitoring devices, electronic healthcare data usually include much less information.

Administrative claims or pharmacy databases record medication dispensation events, including patient identifier, date of event, type of medication, and quantity dispensed, and less frequently daily dosage recommended. The same information may be available for prescription events in electronic medical records used in health care organizations (e.g primary care practices, secondary care centers). In between two dispensing or prescribing events, we don’t know whether and how medication has been used. What we do know is that, if taken as prescribed, the medication supplied at the first event would have lasted a number of days. If the time interval between the two events is longer than this number it is likely that the patient ran out of medication before re-supplying or used less during that time. If the interval is substantially longer or there is no second event, then the patient has probably finished the supply at some point and then discontinued medication. Thus, EHD-based algorithms estimate medication adherence and persistence based on the availability of current supply, under four main assumptions:

(Several other assumptions apply to individual algorithms, and will be discussed later.)

AdhereR was developed to compute adherence and persistence estimates from EHD based on the principles described above. The current version is based on a single data source, therefore an algorithm for initiation (time interval between first prescription and first dispensing event) is not implemented (it is a time difference calculation easy to implement in with basic R functions). The following terms and definitions are used:

Data preparation and example dataset

AdhereR requires a dataset of medication events over a FUW of sufficient length in relation to the recommended treatment duration. To our knowledge, no research has been performed to date on the relationship between FUW length and recommended treatment duration. AdhereR offers the opportunity for answering such methodological questions, but we would hypothesize that the FUW duration also depends on the duration of medication events (shorter durations would allow shorter FUW windows to be informative).

The minimum necessary dataset includes 3 variables for each medication event: patient unique identifier, event date, and duration. Daily dosage and medication type are optional.AdhereR is thus designed to use datasets that have already been extracted from EHD and prepared for calculation. These preliminary steps depend to a large extent on the specific database used and the type of medication and research design. Several general guidelines can be consulted (Arnet et al., 2016; Peterson et al., 2007), as well as database-specific documentation. In essence, these steps should entail:

For demonstration purposes, we included in AdhereR a hypothetical dataset of 1080 medication events from 100 patients in a 2-year FUW. Five variables are included in this dataset:

Table 1 shows the medication events of two example patients: 19 medication events related to two medication types (medA and medB). They were selected to represent two different medication histories. Patient 37 had a stable daily dosage but event duration changes with medication change. Patient 76 had a more variable pattern, including medication, daily dosage and duration changes.

# Load the AdhereR library:
library(AdhereR);
# Select the two patients with IDs 37 and 76 from the built-in dataset "med.events":
ExamplePats <- med.events[med.events$PATIENT_ID %in% c(37, 76), ];
# Display them as pretty markdown table:
knitr::kable(ExamplePats, caption = "<a name=\"Table-1\"></a>**Table 1.** Medication events for two example patients");
Table 1. Medication events for two example patients
PATIENT_ID DATE PERDAY CATEGORY DURATION
14 37 04/10/2036 4 medA 50
15 37 07/30/2036 4 medA 50
16 37 09/15/2036 4 medA 50
17 37 01/02/2037 4 medB 30
18 37 01/31/2037 4 medB 30
19 37 05/09/2037 4 medB 30
20 37 08/13/2037 4 medB 30
21 37 11/09/2037 4 medB 30
813 76 12/13/2035 20 medA 30
814 76 01/18/2036 20 medA 30
815 76 01/23/2036 2 medA 60
816 76 04/25/2036 2 medA 60
817 76 08/08/2036 2 medA 60
818 76 10/03/2036 2 medA 60
819 76 11/29/2036 2 medA 60
820 76 12/21/2036 6 medB 30
821 76 01/05/2037 6 medB 30
822 76 07/13/2037 6 medB 30
823 76 10/11/2037 2 medA 30

Visualization of patient records

A first step towards deciding which algorithm is appropriate for these data is to explore medication histories visually. We do this by creating an object of type CMA0 for the two example patients, and plotting it. This type of plots can of course be created for a much bigger subsample of patients and saved as as a JPEG, PNG, TIFF, EPS or PDF file using R’s plotting system for data exploration.

# Create an object "cma0" of the most basic CMA type, "CMA0":
cma0 <- CMA0(data=ExamplePats, # use the two selected patients
             ID.colname="PATIENT_ID", # the name of the column containing the IDs
             event.date.colname="DATE", # the name of the column containing the event date
             event.duration.colname="DURATION", # the name of the column containing the duration
             event.daily.dose.colname="PERDAY", # the name of the column containing the dosage
             medication.class.colname="CATEGORY", # the name of the column containing the category
             followup.window.start=0,  # FUW start in days since earliest event
             observation.window.start=182, # OW start in days since earliest event
             observation.window.duration=365, # OW duration in days
             date.format="%m/%d/%Y"); # date format (mm/dd/yyyy)
# Plot the object (CMA0 shows the actual event data only):
plot(cma0, # the object to plot
     align.all.patients=TRUE); # align all patients for easier comparison
Figure 1. Medication histories - two example patients

Figure 1. Medication histories - two example patients

We can see that patient 76 had an interruption of more than 100 days between the second and third medB supply and several situations of new supply acquired while the previous supply was not exhausted. Patient 37 had shorter gaps between consecutive events, but very little overlap in supplies. For patient 76, the switch to medB happened while the medA supply was still available, then a switch back to medA happened later, at the end of the second year. For patient 37, there was a single medication switch (to medB) without an overlap at that point.

These observations highlight several decision points in calculating persistence and adherence, which need to be informed by the clinical context of the study:

These decisions therefore need to be taken based on a good understanding of the pharmacological properties of the medication studied, and the most plausible clinical decision-making in routine care. This information can be collected from an advisory committee with relevant expertise (e.g. based on consensus protocols), or (even better) qualitative or survey research on the routine practices in prescribing, dispensing and using that specific medication. Of course, this is not always possible – a second-best option (or even complementary option, if consensus is not reached) is to compare systematically the effects of different analysis choices on the hypotheses tested (e.g. as sensitivity analyses).

Persistence – treatment episodes

An essential first decision is to distinguish between persistence with treatment and quality of implementation (once the patient started treatment – which, as explained above, is assumed in situations when we have only one data source of prescribing or dispensing events). The function compute.treatment.episodes() was developed for this purpose. We provide below an example of how this function can be used.

Let’s imagine that medA and medB are two different types of medication, and clinicians in our advisory committee agree that whenever a health care professional changes the type of medication supplied this should be considered as a new treatment episode; we will specify this as setting the parameter medication.change.means.new.treatment.episode to TRUE.

They also agree that a minumum of 6 months (180 days) need to pass after the end of a medication supply (taken as prescribed) without receiving a new supply in order to be reasonably confident that the patient has discontinued/interrupted the treatment – they can conclude this for example based on an approximate calculation considering that specific medication is usually supplied for 1-2 months, daily dosage is usually 2 to 4 pills a day, and patients often use as low as 1/4 of the recommended dose in a given interval. We will specify this as maximum.permissible.gap = 180, and maximum.permissible.gap.unit = "days". (If in another scenario the clinical information we obtain suggests that the permissible gap should depend on the duration of the last supply, for example 6 times that interval should go by before a discontinuation becoming likely, we can specify this as maximum.permissible.gap = 600, and maximum.permissible.gap.unit = "percent".)

We might also have some clinical confirmation that usually people finish existing supply before starting the new one (carryover.within.obs.window = TRUE), but of course only for the same medication if medA and medB are supplied with a recommendation to start a new treatment immediately (carry.only.for.same.medication = TRUE), take the existing supply based on the new dosage recommendations if these change (consider.dosage.change = TRUE).

The rest of the parameters specify the name of the dataset (here ExamplePats), names of the variables in the dataset (here based on the demo dataset, described above), and the FUW (here the whole 2-year window).

# Compute the treatment episodes for the two patients:
TEs3<- compute.treatment.episodes(ExamplePats,
                                  ID.colname="PATIENT_ID",
                                  event.date.colname="DATE",
                                  event.duration.colname="DURATION",
                                  event.daily.dose.colname="PERDAY",
                                  medication.class.colname="CATEGORY",
                                  carryover.within.obs.window = TRUE, # carry-over into the OW
                                  carry.only.for.same.medication = TRUE, # & only for same type
                                  consider.dosage.change = TRUE, # dosage change starts new episode...
                                  medication.change.means.new.treatment.episode = TRUE, # & type change
                                  maximum.permissible.gap = 180, # & a gap longer than 180 days
                                  maximum.permissible.gap.unit = "days", # unit for the above (days)
                                  followup.window.start = 0, # 2-years FUW starts at earliest event
                                  followup.window.start.unit = "days",
                                  followup.window.duration = 365 * 2,
                                  followup.window.duration.unit = "days",
                                  date.format = "%m/%d/%Y");
knitr::kable(TEs3, 
             caption = "<a name=\"Table-2\"></a>**Table 2.** Example output `compute.treatment.episodes()` function");
Table 2. Example output compute.treatment.episodes() function
PATIENT_ID episode.ID episode.start end.episode.gap.days episode.duration episode.end
37 1 2036-04-10 56 211 2036-11-07
37 2 2037-01-02 122 463 2038-04-10
76 1 2035-12-13 0 374 2036-12-21
76 2 2036-12-21 60 234 2037-08-12
76 3 2037-10-11 32 62 2037-12-12

The function produces a dataset as the one shown in Table 2. It includes each treatment episode for each patient (here 2 episodes for patient 37 and 3 for patient 76) and records the patient ID, episode number, date of episode start, gap days at the end of or after the treatment episode, duration of episode, and episode end date:

Notes:

  1. just the number of gap days after the end of the episode can be computed by keeping all values larger than the permissible gap and by replacing the others by 0,
  2. when medication change represents a new treatment episode, the previous episode ends when the last supply is finished (irrespective of the length of gap compared to a maximum permissible gap); any days before the date of the new medication supply are considered a gap. This maintains consistence with the computation of gaps between episodes (whether they are constructed based on the maximum permissible gap rule or the medication change rule).

This output can be used on its own to study causes and consequences of medication persistence (e.g. by using episode duration in time-to-event analyses). This function is also a basis for the CMA_per_episode class, which is described later in the vignette.

Adherence – continuous multiple interval measures of medication availability/gaps (CMA)

Let’s consider another scenario: medA and medB are alternative formulations of the same chemical molecule, and clinicians agree that they can be used by patients within the same treatment episode. In this case, both patients had a single treatment episode for the whole duration of the follow-up (Table 3). We can therefore compute adherence for any observation window (OW) within these 2 years without any concern that we might confuse quality of implementation with (non-)persistence.

# Compute the treatment episodes for the two patients
# but now a change in medication type does not start a new episode:
TEs4<- compute.treatment.episodes(ExamplePats,
                                  ID.colname="PATIENT_ID",
                                  event.date.colname="DATE",
                                  event.duration.colname="DURATION",
                                  event.daily.dose.colname="PERDAY",
                                  medication.class.colname="CATEGORY",
                                  carryover.within.obs.window = TRUE, 
                                  carry.only.for.same.medication = TRUE,
                                  consider.dosage.change = TRUE,
                                  medication.change.means.new.treatment.episode = FALSE, # here
                                  maximum.permissible.gap = 180,
                                  maximum.permissible.gap.unit = "days",
                                  followup.window.start = 0,
                                  followup.window.start.unit = "days",
                                  followup.window.duration = 365 * 2,
                                  followup.window.duration.unit = "days",
                                  date.format = "%m/%d/%Y");
# Pretty print the events:
knitr::kable(TEs4, 
             caption = "<a name=\"Table-3\"></a>**Table 3.** Alternative scenario output `compute.treatment.episodes()` function");
Table 3. Alternative scenario output compute.treatment.episodes() function
PATIENT_ID episode.ID episode.start end.episode.gap.days episode.duration episode.end
37 1 2036-04-10 122 730 2038-04-10
76 1 2035-12-13 32 730 2037-12-12

Once we clarified that we indeed measure quality of implementation and not (non)-persistence, several CMA classes can be used to compute this specific component of adherence. We will discuss first in turn the simple CMA classes, then present the more complex (or iterated) CMA_per_episode and CMA_sliding_window ones.

The simple CMAs

A first decision to consider when calculating the quality of implementation is what is the appropriate observation window – when it should start and how long it should last? We can see for example that patient 76 had some periods of regular (even overlapping) supplies, and periods when there were some large delays between consecutive medication events. Thus, estimating adherence for a whole 2-year period might be too coarse-grained to mean anything for how patients actually managed their treatment at any particular moment. As mentioned earlier in the Definitions section, EHD don’t have good granularity to start with, so we need to do the best with what we’ve got – and compressing all this information into a single estimate might not be the best solution, at least not the obvious first choice. On the other hand, due to the low granularity, we cannot target very short observation windows either because we simply don’t know what happened every day. This decision needs to be informed again by information collected from the advisory committee or qualitative/quantitative studies in the target population. It also needs to take into account the average duration of medication supply from one event, and the average time interval between two events – which can be examined in exploratory plots (Figure 1) – and the research question and design of the study. For example, if we expect that the quality of implementation reduces in time from the start of a treatment episode, medication is usually supplied for one month, and patients can take up to 4 times as much to use up their supplies, we might want to consider comparing successive 4-month OWs. If we want to examine quality of implementation 6 months before a clinical event (on the clinical assumption that how a patient takes medication in previous 6 months may impact on the probability of a health event occurring or not), we might want to consider an OW start 6 months before the event, and a 6-month duration. The posibilities here are endless, and research on the impact of different analysis choices on substantive results is still scarce. When the consensus is not reached based on the available information, one or more parametrisations can be compared – and formulated as research questions.

For demonstration purposes, let’s imagine a scenario when an adherence intervention takes place 6 months (182 days) after the start of the treatment episode, and we hypothesize that it will improve the quality of implementation in the next year (365 days) in the intervention group compared to the control group. We can specify this as followup.window.start=0, observation.window.start=182, and observation.window.duration=365 (we can of course divide this interval into shorter windows and compare the two groups in terms of longitudinal changes in adherence, as we shall see later, but for the moment let’s stick to a global 1-year estimate). We have 9 CMA classes that can produce very different estimates of the quality of implementation, the first eight have been described by Vollmer and colleagues (2012) as applied to randomized controlled trials. We implemented them in AdhereR based on the authors’ description, and in essence are defined by 4 parameters:

  1. how is the OW delimited (whether time intervals before the first event and after the last event are considered),
  2. whether CMA values are capped at 100%,
  3. whether medication oversupply is carried over to the next event interval, and
  4. whether medication available before a first event is considered in supply calculations or OW definition.

CMA1

CMA1 is the simplest method, often described in the literature as the medication possession ratio (MPR). It simply adds up the duration of all medication events within the OW, excluding the last event, and divides this by the number of days between the first and last event (multiplied by 100 to obtain a percentage). Thus, it can be higher than 1 (or 100% adherence) and, if the OW does not start and end with a medication event for all patients, it can actually refer to different lengths of time within the OW for different patients. For example, for patient 76 below CMA1 is computed for the period starting with the first event in the highlighted interval and ending at the date if the last event – thus, it considers only 4 events with considerable overlaps and results in a CMA1 of 140%, indicating overuse.

Creating an object of class CMA1 with various parameters automatically performs the estimation of CMA1 for all the patients in the dataset; moreover, the object is smart enough to allow the appropriate printing and plotting. The object includes all the parameter values with which it was created, as well as the CMA data.frame, which is the main result, with two columns: patient ID and the corresponding CMA estimate. The CMA estimates appear as ratios, but can be trivially transformed into percentages and rounded, as we did for patient 76 below (rounded to 2 decimals). The plots show the CMA as percentage rounded to 1 decimal.

# Create the CMA1 object with the given parameters:
cma1 <- CMA1(data=ExamplePats,
             ID.colname="PATIENT_ID",
             event.date.colname="DATE",
             event.duration.colname="DURATION",
             followup.window.start=0, observation.window.start=182, 
             observation.window.duration=365,
             date.format="%m/%d/%Y");
# Display the summary:
cma1
## CMA1:
##   "The ratio of days with medication available in the observation window excluding the last event; durations of all events added up and divided by number of days from first to last event, possibly resulting in a value >1.0"
##   [
##     ID.colname = PATIENT_ID
##     event.date.colname = DATE
##     event.duration.colname = DURATION
##     followup.window.start = 0
##     followup.window.start.unit = days
##     followup.window.duration = 730
##     followup.window.duration.unit = days
##     observation.window.start = 182
##     observation.window.start.unit = days
##     observation.window.duration = 365
##     observation.window.duration.unit = days
##     date.format = %m/%d/%Y
##     CMA = CMA results for 2 patients
##   ]
##   DATA: 19 (rows) x 5 (columns) [2 patients].
# Display the estimated CMA table:
cma1$CMA
##   PATIENT_ID       CMA
## 1         37 0.4035874
## 2         76 1.4000000
# and equivalently using an accessor function:
getCMA(cma1);
##   PATIENT_ID       CMA
## 1         37 0.4035874
## 2         76 1.4000000
# Compute the CMA value for patient 76, as percentage rounded at 2 digits:
round(cma1$CMA[cma1$CMA$PATIENT_ID== 76, 2]*100, 2)
## [1] 140
# Plot the CMA:
# The legend shows the actual duration, the days covered and gap days, 
# the drug (medication) type, the FUW and OW, and the estimated CMA.
plot(cma1, 
     patients.to.plot=c("76"), # plot only patient 76 
     legend.x=520); # place the legend in a nice way
Figure 2. Simple CMA 1

Figure 2. Simple CMA 1

CMA2

Thus, CMA1 assumes that there is a treatment episode within the OW (shorter or equal to the OW) when the patient used the medication, and every new medication event happened when the previous supply finished (possibly due to overuse). These assumptions rarely fit with real life use patterns. One limitation is not considering the last event – which represents almost a half of the OW in the case of patient 76.

To address this limitation, CMA2 includes the duration of the last event in the numerator and the period from the last event to the end of the OW in the denominator. Thus, the estimate Figure 3 is 77.9%, more in line with the medication history of this patient in the year after the intervention.

cma2 <- CMA2(data=ExamplePats, # we're estimating CMA2 now!
             ID.colname="PATIENT_ID",
             event.date.colname="DATE",
             event.duration.colname="DURATION",
             followup.window.start=0, observation.window.start=182, 
             observation.window.duration=365,
             date.format="%m/%d/%Y");
plot(cma2, 
     patients.to.plot=c("76"),  
     show.legend=FALSE); # don't show legend to avoid clutter (see above)
Figure 3. Simple CMA 2

Figure 3. Simple CMA 2

CMA3 and CMA4

Both CMA1 and CMA2 can be higher that 1 (100% adherence) based on the assumption that medication supply is finished until the last event (CMA1) or the end of the OW (CMA2). But sometimes this is not plausible, because patients can refill their supply earlier (for example when going on holidays) and overuse is a less frequent behaviour for some medications (when side effects are considerable for overuse, or medications are expensive). Or it may be that it does not matter whether patients use 100% or more that 100% of their medication, the therapeutic effect is the same with no risks or side effects. Again, this is a matter of inquiry to the advisory committee or investigation in the target population.

If it is likely that implementation does not exceed 100% (or does not make a difference if it does), CMA3 and CMA4 below adjust for this by capping CMA1 and CMA2 respectively to 100%. As shown in Figures 4 and 5, CMA3 is now capped at 100%, and CMA4 remains the same as CMA2 (because it was already lower than 100%).

cma3 <- CMA3(data=ExamplePats, # we're estimating CMA3 now!
             ID.colname="PATIENT_ID",
             event.date.colname="DATE",
             event.duration.colname="DURATION",
             followup.window.start=0, observation.window.start=182, 
             observation.window.duration=365,
             date.format="%m/%d/%Y");
plot(cma3, patients.to.plot=c("76"), show.legend=FALSE);
Figure 4. Simple CMA 3

Figure 4. Simple CMA 3

cma4 <- CMA4(data=ExamplePats, # we're estimating CMA4 now!
             ID.colname="PATIENT_ID",
             event.date.colname="DATE",
             event.duration.colname="DURATION",
             followup.window.start=0, observation.window.start=182, 
             observation.window.duration=365,
             date.format="%m/%d/%Y");
plot(cma4,patients.to.plot=c("76"), show.legend=FALSE);
Figure 5. Simple CMA 4

Figure 5. Simple CMA 4

CMA5 and CMA6

All CMAs from 1 to 4 have a major limitation: they don’t take into account the timing of the events. But if there is a large gap between two events it is more likely that the person had used the medication less than prescribed at least in part of that interval. Just capping the values as in CMA3 and CMA4 does not account for that likely reduction in adherence – two patients with the same quantity of supply will have the same percentage of adherence even if one has had substantial delays in supply at some points and the other supplied in time.

To adjust for this, CMA5 and CMA6 provide alternative calculations to CMA1 and CMA2 respectively. Thus, we instead calculate the number of gap days, extract it from the total time interval, and divide this value by the total time interval (first to last event in CMA5, and first event to end of OW in CMA6). By considering the gaps, we now need to decide whether to control for how any remaining supply is used when a new supply is obtained. Two additional parameters are included here: carry.only.for.same.medication and consider.dosage.change. Both are set here as FALSE, to specify the fact that carry over should always happen irrespective of what medication is supplied, and that the duration of the remaining supply should be modified if the dosage recommendations are changed with a new medication event. As shown in Figures 6 and 7, these alternative calculations do not make any difference for patient 76, because there are no gaps between the 5 events in the OW highighted. There could be, however, situations in which large gaps between some events in the OW result in lower CMA estimates when considering timing of events.

cma5 <- CMA5(data=ExamplePats, # we're estimating CMA5 now!
             ID.colname="PATIENT_ID",
             event.date.colname="DATE",
             event.duration.colname="DURATION",
             event.daily.dose.colname="PERDAY",
             medication.class.colname="CATEGORY",
             carry.only.for.same.medication=FALSE, # carry-over across medication types
             consider.dosage.change=FALSE, # don't consider canges in dosage
             followup.window.start=0, observation.window.start=182, 
             observation.window.duration=365,
             date.format="%m/%d/%Y");
plot(cma5,patients.to.plot=c("76"), show.legend=FALSE);
Figure 6. Simple CMA 5

Figure 6. Simple CMA 5

cma6 <- CMA6(data=ExamplePats, # we're estimating CMA6 now!
             ID.colname="PATIENT_ID",
             event.date.colname="DATE",
             event.duration.colname="DURATION",
             event.daily.dose.colname="PERDAY",
             medication.class.colname="CATEGORY",
             carry.only.for.same.medication=FALSE,
             consider.dosage.change=FALSE,
             followup.window.start=0, observation.window.start=182, 
             observation.window.duration=365,
             date.format="%m/%d/%Y");
plot(cma6,patients.to.plot=c("76"), show.legend=FALSE);