Calculate several polypharmacy indicators

Description

This package analyses prescription drug deliveries to calculate several indicators of polypharmacy corresponding to the various definitions found in the literature.

Details

It is essential to understand the underlying concepts used to calculate the various polypharmacy indicators to adequately use this package.
The core of the package is the data_process() function that creates a data.table of drug treatments by restructuring the drug delivery records (usually extracted from a pharmacy or a health insurance information system) into continuous periods of drug availability (called drug treatments) for every individual over the whole period of the study period. This process relies on several user-defined arguments such as the grace periods between renewals or the longest treatment duration that an individual may accumulate through successive early renewals.

Then, each polypharmacy indicator can be computed using the corresponding function (ind_simult(), ind_stdcumul(), ind_wcumul(), ind_stdcontinuous(), ind_ucontinuous()) or using the wrapper function indicators() to select the desired indicator(s) to be calculated at once.

Prior to running data_process() the user may need to pre-process the table of original drug delivery records to break down combination drug into their individual components (drugs_bkdn()) and/or to overwrite the delivery durations of some specified drugs with constant user-defined durations (cst_trt_dur()).

Author(s)

Maintainer: Guillaume Boucher guiboucher8@gmail.com

Authors:

• Bernard Candas bernard.candas.1@gmail.com

• Houssem Missaoui missaoui_houssem@hotmail.fr

Other contributors:

• Caroline Sirois caroline.sirois@pha.ulaval.ca [scientific advisor]

• Marc Simard marc.simard@inspq.qc.ca [scientific advisor]

See Also

Useful links:

• Report bugs at https://github.com/guiboucher/polypharmacy/issues

Argcheck

Description

Add an error in the environment created by newArgCheck.

Usage

addError(msg, argcheck)

Arguments

Value

Add an error in the environment argcheck.

Argcheck

Description

Add a message in the environment created by newArgCheck.

Usage

addMessage(msg, argcheck)

Arguments

Value

Add a message in the environment argcheck.

Argcheck

Description

Add a warning in the environment created by newArgCheck.

Usage

addWarning(msg, argcheck)

Arguments

Value

Add a warning in the environment argcheck.

Constant delivery duration drugs

Description

Overwrites the recorded delivery durations of specific drugs with constant durations as specified in a user-provided table.

Usage

cst_deliv_duration(
  Rx_deliv,
  Rx_drug_code,
  Rx_deliv_dur,
  Cst_deliv_dur,
  Cst_drug_code,
  Cst_duration
)

Arguments

Value

data.table of the same structure as Rx_deliv.

Examples

# With matches
rx1 <- data.frame(id = c(1, 1, 2, 2, 2), code = c("A", "B", "B", "C", "D"),
                  duration = as.integer(c(30, 15, 15, 7, 90)))
cst1 <- data.frame(CODES = c("B", "D"), DURATION = as.integer(c(45, 60)))
cst_deliv_duration(
  Rx_deliv = rx1, Rx_drug_code = "code", Rx_deliv_dur = "duration",
  Cst_deliv_dur = cst1, Cst_drug_code = "CODES", Cst_duration = "DURATION"
)

# No matches
rx2 <- data.frame(id = c(1, 1, 2, 2, 2), code = c("A", "B", "B", "C", "D"),
                  duration = as.integer(c(30, 15, 15, 7, 90)))
cst2 <- data.frame(CODES = c("E", "F"), DURATION = as.integer(c(45, 60)))
cst_deliv_duration(
  Rx_deliv = rx2, Rx_drug_code = "code", Rx_deliv_dur = "duration",
  Cst_deliv_dur = cst2, Cst_drug_code = "CODES", Cst_duration = "DURATION"
)

Verification

Description

Arguments verification for cst_deliv_duration.

Usage

cst_deliv_duration.verif_args(
  Rx_deliv,
  Rx_drug_code,
  Rx_deliv_dur,
  Cst_deliv_dur,
  Cst_drug_code,
  Cst_duration
)

Arguments

Value

Messages, warnings or errors

Create the table of the drug treatments

Description

Reads a table of successive drug delivery records (usually extracted from a pharmacy or a health insurance information system) and creates the table required for the calculation of the polypharmacy indicators by applying various user-defined arguments, incorporating hospital stays into the treatment periods and reconstruct continuous treatment periods by merging quasi continuous and/or overlapping drugs deliveries.

Usage

data_process(
  Rx_deliv,
  Rx_id,
  Rx_drug_code,
  Rx_drug_deliv,
  Rx_deliv_dur,
  Cohort = NULL,
  Cohort_id = NULL,
  Hosp_stays = NULL,
  Hosp_id = NULL,
  Hosp_admis = NULL,
  Hosp_discharge = NULL,
  study_start = NULL,
  study_end = NULL,
  grace_fctr = 0.5,
  grace_cst = 0,
  max_reserve = NULL,
  cores = parallel::detectCores(logical = FALSE),
  ...
)

Arguments

Details

Variables:

• Rx_id, Cohort_id and Hosp_id columns must be of the same class (integer, numeric, character, ...).

• Rx_drug_deliv, Hosp_admis and Hosp_discharge can be 1) as.Date('yyyy-mm-dd'), 2) as.character('yyyy-mm-dd') or 3) as.integer() where 0 is January 1^st, 1970.

Arguments:

• study_start and study_end can be 1) as.Date('yyyy-mm-dd'), 2) as.character('yyyy-mm-dd') or 3) as.integer() where 0 is January 1^st, 1970.

Hospital stays:
Drug availability is assumed to continue during the hospital stay as it is on the day prior admission. The patient is assumed to resume the consumption of the drugs delivered by community pharmacists (as recorded in Rx_deliv) the day after hosp_discharge.
Grace period is always zero (0) for hospital stays.

Run-in period:
A run-in period is necessary to account for the medications that are available to the individuals on the day of study_start. It is recommended to include a run-in period of about 6 months (e.g. 7 months to account for possible delays) as some drugs are delivered for up to 6 months at once.

Grace period:
The grace period is used to determine if two successive deliveries can be considered as a continuous treatment even if there is a gap of several days for which no treatment is apparently available. Two successive deliveries of an identical drug are considered part of a single continuous treatment if the next delivery doesn’t occur more than grace_cst + (grace_fctr × Rx_deliv_dur) days after the end of the latest drug delivery. The availability of extra drugs accumulated over the successive deliveries is accounted for prior to evaluating the duration of the gap between deliveries.

Performance
For better performance, date columns are converted to integer numbers.

...
verif_cols=FALSE : For better performance, you can avoid columns class checking with verif_cols=FALSE. Not recommended.

Value

data.table with four (4) variables:

• The individual unique identifier which name is defined by Rx_id.

• The drug unique identifier which name is defined by Rx_drug_code.

• tx_start: The date of initiation of the reconstructed continued treatment (format as date).

• tx_end: The date of the last day of the reconstructed continued treatment (format as date).

Examples

### Standard evaluation
data_process(
  Rx_deliv = sample_Rx_unprocessed, Rx_id = "id", Rx_drug_code = "code",
  Rx_drug_deliv = "start", Rx_deliv_dur = "duration",
  cores = 1L
)

### Hospitalisation stays
rx1 <- data.frame(
  id = c(1L, 3:8),
  code = LETTERS[c(1, 3:8)],
  date = as.Date(c("2001-01-15", "2003-03-15", "2004-04-15", "2005-05-15",
                   "2006-06-15", "2007-07-15", "2008-08-15")),
  duration = 10L
)
hosp1 <- data.frame(
  ID = 3:8,
  ADM = as.Date(c("2003-03-10", "2004-04-25", "2005-05-12",
                  "2006-06-20", "2007-07-26", "2008-08-01")),
  DEP = as.Date(c("2003-03-14", "2004-04-30", "2005-05-17",
                  "2006-06-30", "2007-07-30", "2008-08-13"))
)
data_process(
  Rx_deliv = rx1, Rx_id = "id", Rx_drug_code = "code",
  Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
  Hosp_stays = hosp1, Hosp_id = "ID", Hosp_admis = "ADM", Hosp_discharge = "DEP",
  study_start = "2001-01-01", study_end = "2008-12-31",
  cores = 1L
)
# Many drug codes
rx2 <- data.frame(
  id = 1L,
  code = c(111L, 222L, 222L, 333L, 444L),
  date = as.Date(c("2001-01-15", "2002-02-15", "2002-03-01", "2004-04-07", "2004-05-05")),
  duration = as.integer(c(10, 10, 10, 30, 10))
)
hosp2 <- data.frame(
  id = 1L,
  adm = as.Date(c("2000-01-01", "2000-01-15", "2001-01-01", "2002-02-23", "2004-04-15")),
  dep = as.Date(c("2000-01-31", "2000-01-31", "2001-01-10", "2002-02-28", "2004-05-15"))
)
data_process(
  Rx_deliv = rx2, Rx_id = "id", Rx_drug_code = "code",
  Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
  Hosp_stays = hosp2, Hosp_id = "id", Hosp_admis = "adm", Hosp_discharge = "dep",
  study_start = "2001-01-01", study_end = "2008-12-31",
  cores = 1L
)

### Study dates - start and end
rx3 <- data.frame(id = 1:3,
                  code = "A",
                  date = as.Date(c("2020-01-01", "2020-06-06", "2020-12-22")),
                  duration = 10L)
# NULLs
data_process(Rx_deliv = rx3, Rx_id = "id", Rx_drug_code = "code",
             Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
             study_start = NULL, study_end = NULL,
             cores = 1)
# Not NULLs
data_process(Rx_deliv = rx3, Rx_id = "id", Rx_drug_code = "code",
             Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
             study_start = "2020-06-10", study_end = NULL,
             cores = 1)
data_process(Rx_deliv = rx3, Rx_id = "id", Rx_drug_code = "code",
             Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
             study_start = NULL, study_end = "2020-06-10",
             cores = 1)
data_process(Rx_deliv = rx3, Rx_id = "id", Rx_drug_code = "code",
             Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
             study_start = "2020-01-05", study_end = "2020-12-25",
             cores = 1)

### Grace factor
rx4 <- data.frame(id = c(rep(1, 3), rep(2, 3)),
                  code = "A",
                  date = as.Date(c("2000-01-01", "2000-01-17", "2000-01-31",
                                   "2000-06-01", "2000-06-23", "2000-07-16")),
                  duration = as.integer(c(10, 10, 10, 15, 15, 15)))
# 50% of duration
data_process(Rx_deliv = rx4, Rx_id = "id", Rx_drug_code = "code",
             Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
             grace_fctr = 0.5,
             cores = 1)
# 0% of duration
data_process(Rx_deliv = rx4, Rx_id = "id", Rx_drug_code = "code",
             Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
             grace_fctr = 0,
             cores = 1)

### Grace constant
rx5 <- data.frame(id = 1,
                  code = "A",
                  date = as.Date(c("2000-01-01", "2000-01-14", "2000-01-25")),
                  duration = as.integer(c(10, 10, 6)))
# 2 days
data_process(Rx_deliv = rx5, Rx_id = "id", Rx_drug_code = "code",
             Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
             grace_fctr = 0, grace_cst = 2,
             cores = 1)
# 3 days
data_process(Rx_deliv = rx5, Rx_id = "id", Rx_drug_code = "code",
             Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
             grace_fctr = 0, grace_cst = 3,
             cores = 1)

### Max reserve
rx6 <- data.frame(id = as.integer(c(1, 1, 3, 3, 3, 5, 5)),
                  code = "A",
                  date = as.Date(c("2000-01-01", "2000-01-31",
                                   "2000-03-03", "2000-03-15", "2000-03-30",
                                   "2000-05-05", "2000-05-05")),
                  duration = as.integer(c(30, 30,
                                          30, 30, 30,
                                          90, 90)))
# 0 days
data_process(Rx_deliv = rx6, Rx_id = "id", Rx_drug_code = "code",
             Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
             study_start = NULL, study_end = "2000-12-31",
             grace_fctr = 0, grace_cst = 0,
             max_reserve = 0,
             cores = 1)
# 60 days
data_process(Rx_deliv = rx6, Rx_id = "id", Rx_drug_code = "code",
             Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
             study_start = NULL, study_end = "2000-12-31",
             grace_fctr = 0, grace_cst = 0,
             max_reserve = 60,
             cores = 1)
# Inf days
data_process(Rx_deliv = rx6, Rx_id = "id", Rx_drug_code = "code",
             Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
             study_start = NULL, study_end = "2000-12-31",
             grace_fctr = 0, grace_cst = 0,
             max_reserve = NULL,
             cores = 1)

### Combine Hospital stays and Grace factor
rx7 <- data.frame(id = c(1L, 1L, 1L, 2L),
                  code = "A",
                  date = c("2000-01-01", "2000-02-20", "2000-04-11", "2002-02-02"),
                  duration = as.integer(c(30, 30, 30, 15)))
hosp7 <- data.frame(id = 1L,
                    adm = c("2000-01-11", "2000-02-21"),
                    dep = c("2000-01-15", "2000-02-25"))
data_process(Rx_deliv = rx7, Rx_id = "id", Rx_drug_code = "code",
             Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
             Hosp_stays = hosp7, Hosp_id = "id",
             Hosp_admis = "adm", Hosp_discharge = "dep",
             study_start = "2000-01-01", study_end = "2002-12-31",
             grace_fctr = 0.5, grace_cst = 0, max_reserve = NULL,
             cores = 1)

Data Process

Description

data_process but with only 1 core. To use in the multicores process.

Usage

data_process.1_core(
  Rx_deliv,
  Rx_id,
  Rx_drug_code,
  Rx_drug_deliv,
  Rx_deliv_dur,
  Cohort = NULL,
  Cohort_id = NULL,
  Hosp_stays = NULL,
  Hosp_id = NULL,
  Hosp_admis = NULL,
  Hosp_discharge = NULL,
  study_start = NULL,
  study_end = NULL,
  grace_fctr = 0.5,
  grace_cst = 0,
  max_reserve = NULL
)

Arguments

Value

data.table with four (4) variables:

• The individual unique identifier which name is defined by Rx_id.

• The drug unique identifier which name is defined by Rx_drug_code.

• tx_start: The date of initiation of the reconstructed continued treatment (format as date).

• tx_end: The date of the last day of the reconstructed continued treatment (format as date).

Verification

Description

Arguments verification for data_process.

Usage

data_process.verif_args(
  Rx_deliv,
  Rx_id,
  Rx_drug_code,
  Rx_drug_deliv,
  Rx_deliv_dur,
  Cohort,
  Cohort_id,
  Hosp_stays,
  Hosp_id,
  Hosp_admis,
  Hosp_discharge,
  study_start,
  study_end,
  grace_fctr,
  grace_cst,
  max_reserve,
  verif_cols,
  cores
)

Arguments

Value

Messages, warnings or errors

Translate combination drug deliveries into several single active ingredients

Description

Replaces each combination drug into several deliveries of elementary active ingredients according to a user-provided correspondence table.

Usage

drug_bkdn(Rx_deliv, Rx_drug_code, Combn_drugs, Combn_drug_code, Combn_act_code)

Arguments

Value

data.table of the same structure as Rx_deliv.

Examples

### With matches
rx1 <- data.frame(id = c(1L, 1L, 2L, 2L, 2L),
                  code = c(159L, 753L, 123L, 456L, 789L))
split1 <- data.frame(code = c(159L, 159L, 456L, 456L, 456L),
                     splitcode = c(1591L, 1592L, 4567L, 4568L, 4569L))
drug_bkdn(Rx_deliv = rx1, Rx_drug_code = "code",
          Combn_drugs = split1, Combn_drug_code = "code", Combn_act_code = "splitcode")

### No matches
rx2 <- data.frame(id = c(1L, 1L, 2L, 2L, 2L),
                  code = c(159L, 753L, 123L, 456L, 789L))
split2 <- data.frame(CODE = c(147L, 147L, 963L, 963L, 963L),
                     SPLITCODE = c(1471L, 1472L, 9637L, 9638L, 9639L))
drug_bkdn(Rx_deliv = rx2, Rx_drug_code = "code",
          Combn_drugs = split2, Combn_drug_code = "CODE", Combn_act_code = "SPLITCODE")

Verification

Description

Arguments verification for drug_bkdn.

Usage

drug_bkdn.verif_args(
  Rx_deliv,
  Rx_drug_code,
  Combn_drugs,
  Combn_drug_code,
  Combn_act_code
)

Arguments

Value

Messages, warnings or errors

Argcheck

Description

Create an environment where to put errors, messages or warnings.

Usage

finishArgCheck(argcheck)

Arguments

Value

Messages, warnings or errors inserted in newArgCheck.

Assess polypharmacy based on the daily simultaneous consumption of medications

Description

Calculates various metrics measuring the number of distinct medications consumed daily for every individual of the study cohort over the study period and provides cohort descriptive statistics on those metrics.

Usage

ind_simult(
  processed_tab,
  individual_stats = c("mean", "min", "median", "max"),
  stats = c("mean", "sd", "min", "p5", "p10", "p25", "median", "p75", "p90", "p95",
    "max"),
  calendar = FALSE,
  cores = parallel::detectCores()
)

Arguments

Details

individual_stats & stats: Possible values are

• 'mean', 'min', 'median', 'max', 'sd';

• 'pX' where X is an integer value in ]0, 100];

• 'q1'='p25', 'q2'='p50'='median', q3='p75'.

Value

list:

• indic: data.table indicating each stats (columns) for each individual_stats (rows).

• stats_id: data.table indicating each individual_stats for each individuals (all cohort).

• min_conso: data.table indicating each stats for the number of days where an individual consume at least X drugs.

• calendar: If calendar=TRUE, data.table indicating the number of drugs consumed for each day (only for individuals who has at least 1 day with 1 drug consumption).

Examples

rx1 <- data.frame(id = c(1, 1, 2),
                  code = c("A", "B", "A"),
                  date = c("2000-01-01", "2000-01-04", "2000-01-08"),
                  duration = c(5, 7, 5))
cohort1 <- data.frame(id = as.numeric(1:3),
                      age = c(45, 12, 89),
                      sex = c("F", "F", "M"))
rx_proc1 <- data_process(Rx_deliv = rx1, Rx_id = "id", Rx_drug_code = "code",
                         Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
                         Cohort = cohort1, Cohort_id = "id",
                         study_start = "2000-01-01", study_end = "2000-01-15",
                         cores = 1)
dt_ind_simult <- ind_simult(rx_proc1, calendar = TRUE, cores = 1)

Assess polypharmacy based on the number of medications that is consumed both during the initial and the final period of the study period

Description

Calculates the number of distinct medications that are consumed both during the initial and the final period of the overall study period by every individual of the study cohort and provides cohort descriptive statistics on this indicator.

Usage

ind_stdcontinuous(
  processed_tab,
  pdays,
  stats = c("mean", "sd", "min", "p5", "p10", "p25", "median", "p75", "p90", "p95",
    "max")
)

Arguments

Details

stats: Possible values are

• 'mean', 'min', 'median', 'max', 'sd';

• 'pX' where X is an integer value in ]0, 100];

• 'q1'='p25', 'q2'='p50'='median', q3='p75'.

Value

list:

• indic: data.table indicating each stats (columns).

• stats_id: data.table indicating the number of drugs use for each individual (all cohort).

Examples

rx1 <- data.frame(id = c(1, 1, 1, 2, 3),
                  code = c("A", "A", "B", "A", "A"),
                  date = c("2000-01-01", "2000-01-22", "2000-01-10", "2000-01-01", "2000-01-20"),
                  duration = c(10, 10, 22, 31, 12))
cohort1 <- data.frame(id = as.numeric(1:4),
                      age = c(45, 12, 89, 31),
                      sex = c("F", "F", "M", "M"))
rx_proc1 <- data_process(Rx_deliv = rx1, Rx_id = "id", Rx_drug_code = "code",
                         Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
                         Cohort = cohort1, Cohort_id = "id",
                         study_start = "2000-01-01", study_end = "2000-01-31",
                         cores = 1)
dt_ind_stdcontinuous <- ind_stdcontinuous(processed_tab = rx_proc1, pdays = 10)

Assess polypharmacy based on the average number of distinct medications consumed over successive periods of time of equal length

Description

Averages the number of distinct medications that are consumed by every individual during successive periods of time of equal length and provides cohort descriptive statistics on this indicator.

Usage

ind_stdcumul(
  processed_tab,
  nPeriod = 1,
  stats = c("mean", "sd", "min", "p5", "p10", "p25", "median", "p75", "p90", "p95",
    "max")
)

Arguments

Details

stats: Possible values are

• 'mean', 'min', 'median', 'max', 'sd';

• 'pX' where X is an integer value in ]0, 100];

• 'q1'='p25', 'q2'='p50'='median', q3='p75'.

Value

list:

• indic: data.table indicating each stats (columns).

• stats_id: data.table. For each individual (all cohort), indicate the number of drug use per period (perX where X is a number between 1 and nPeriod) and the mean of the periods (nRx).

Examples

rx1 <- data.frame(id = c(1, 1, 1, 2),
                  code = c("A", "B", "C", "A"),
                  date = c("2000-01-01", "2000-01-01", "2000-01-26", "2000-01-17"),
                  duration = c(30, 5, 5, 10))
cohort1 <- data.frame(id = as.numeric(1:3),
                      age = c(45, 12, 89),
                      sex = c("F", "F", "M"))
rx_proc1 <- data_process(Rx_deliv = rx1, Rx_id = "id", Rx_drug_code = "code",
                         Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
                         Cohort = cohort1, Cohort_id = "id",
                         study_start = "2000-01-01", study_end = "2000-01-30",
                         cores = 1)
# 1 period
dt_ind_stdcumul_per1 <- ind_stdcumul(processed_tab = rx_proc1, nPeriod = 1)
# 3 periods
dt_ind_stdcumul_per3 <- ind_stdcumul(processed_tab = rx_proc1, nPeriod = 3)

Assess polypharmacy based on the uninterrupted consumption of distinct medications over the study period

Description

Calculates the number of distinct medications that are consumed everyday with no interruption over the study period by every individual and provides cohort descriptive statistics on this indicator.

Usage

ind_ucontinuous(
  processed_tab,
  stats = c("mean", "sd", "min", "p5", "p10", "p25", "median", "p75", "p90", "p95",
    "max")
)

Arguments

Details

stats: Possible values are

• 'mean', 'min', 'median', 'max', 'sd';

• 'pX' where X is an integer value in ]0, 100];

• 'q1'='p25', 'q2'='p50'='median', q3='p75'.

Value

list:

• indic: data.table indicating each stats (columns).

• stats_id: data.table indicating the number of drugs use for each individual (all cohort).

Examples

rx1 <- data.frame(id = c(1, 1, 1, 2),
                  code = c("A", "B", "C", "A"),
                  date = c("2000-01-01", "2000-01-01", "2000-01-26", "2000-01-17"),
                  duration = c(30, 29, 5, 10))
cohort1 <- data.frame(id = as.numeric(1:3),
                      age = c(45, 12, 89),
                      sex = c("F", "F", "M"))
rx_proc1 <- data_process(Rx_deliv = rx1, Rx_id = "id", Rx_drug_code = "code",
                         Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
                         Cohort = cohort1, Cohort_id = "id",
                         study_start = "2000-01-01", study_end = "2000-01-30",
                         cores = 1)
dt_ind_ucontinuous <- ind_ucontinuous(processed_tab = rx_proc1)

Assess polypharmacy based on the number of distinct medications consumed weighted to their respective duration of consumption

Description

Calculates the number of distinct medications weighted by the duration of consumption that are consumed by every individual and provides cohort descriptive statistics on this indicator.

Usage

ind_wcumul(
  processed_tab,
  stats = c("mean", "sd", "min", "p5", "p10", "p25", "median", "p75", "p90", "p95",
    "max")
)

Arguments

Details

stats: Possible values are

• 'mean', 'min', 'median', 'max', 'sd';

• 'pX' where X is an integer value in ]0, 100];

• 'q1'='p25', 'q2'='p50'='median', q3='p75'.

Value

list:

• indic: data.table indicating each stats (columns).

• stats_id: data.table indicating the number of drugs use for each individual (all cohort).

Examples

rx1 <- data.frame(id = c(1, 1, 1, 2),
                  code = c("A", "B", "C", "A"),
                  date = c("2000-01-01", "2000-01-01", "2000-01-26", "2000-01-17"),
                  duration = c(30, 5, 5, 10))
cohort1 <- data.frame(id = as.numeric(1:3),
                      age = c(45, 12, 89),
                      sex = c("F", "F", "M"))
rx_proc1 <- data_process(Rx_deliv = rx1, Rx_id = "id", Rx_drug_code = "code",
                         Rx_drug_deliv = "date", Rx_deliv_dur = "duration",
                         Cohort = cohort1, Cohort_id = "id",
                         study_start = "2000-01-01", study_end = "2000-01-30",
                         cores = 1)
dt_ind_wcumul <- ind_wcumul(processed_tab = rx_proc1)

Provide several polypharmacy indicators at once

Description

Wrapper function to run sequentially various polypharmacy functions on a single set of data. Each function corresponds to a different definition of polypharmacy.

Usage

indicators(
  processed_tab,
  stats = c("mean", "sd", "min", "p5", "p10", "p25", "median", "p75", "p90", "p95",
    "max"),
  method = c("ind_simult", "ind_stdcumul", "ind_wcumul", "ind_stdcontinuous",
    "ind_ucontinuous"),
  stdconti_pdays = 90,
  simult_ind_stats = c("mean", "min", "median", "max"),
  simult_calendar = FALSE,
  stdcumul_nPeriod = c(1, 3),
  cores = parallel::detectCores()
)

Arguments

Details

stats & simult_ind_stats: Possible values are

• 'mean', 'min', 'median', 'max', 'sd';

• 'pX' where X is an integer value in ]0, 100];

• 'q1'='p25', 'q2'='p50'='median', q3='p75'.

method: Possible values are

• 'ind_simult' to assess polypharmacy based on the daily simultaneous consumption of medication.

• ⁠'ind_stdcumul⁠ to assess polypharmacy based on the cumulative number of distinct medications consumed over a given period of time (i.e. the standard definition).

• 'ind_wcumul' to assess polypharmacy based on the cumulative number of distinct medication consumed over a given period of time, weighted by the duration of consumption of each medication.

• 'ind_stdcontinuous' to assess polypharmacy based on the number of medications that are consumed both during the initial and the final period of the study period.

• 'ind_ucontinuous' to assess polypharmacy based on the uninterrupted consumption of distinct medications over the study period.

Value

list of the values returned by every function listed in the method argument.

Examples

dt_indic <- indicators(
  processed_tab = sample_Rx_processed,
  stats = c('mean', 'sd', 'min', 'p5', 'p10', 'p25', 'median', 'p75', 'p90', 'p95', 'max'),
  method = c('ind_simult', 'ind_stdcumul', 'ind_wcumul', 'ind_stdcontinuous', 'ind_ucontinuous'),
  stdconti_pdays = c(30, 90),
  simult_ind_stats = c('mean', 'min', 'median', 'max'),
  simult_calendar = TRUE,
  stdcumul_nPeriod = c(1, 3),
  cores = 1
)

Argcheck

Description

Create an environment where to put errors, messages or warnings.

Usage

newArgCheck()

Value

environment

Utils

Description

Delete NAs and NaNs.

Usage

rmNA(x)

Arguments

Value

x without NAs or NaNs.

Examples

x <- c(1:10, NA, NaN)
rmNA(x)

Table: Processed unprocessed table

Description

This table is provided to users of this package for training purposes. It is created by using data_process function on sample_Rx_unprocessed data.

Usage

sample_Rx_processed

Format

A data.table with 6792 obs and 4 variables:

id

Individual unique identifier.

code

Medication unique identifier.

tx_start

The date of initiation of the reconstructed continued treatment (format as date).

tx_end

The end date of the reconstructed continued treatment (format as date).

Table: Prescription drugs deliveries

Description

A sample table of prescription drugs deliveries provided to users of this package for training purposes. It contains the raw information that leads to sample_Rx_processed when processes by the data_process function.

Usage

sample_Rx_unprocessed

Format

A data.table with 17060 obs and 4 variables:

id

Individual unique identifier.

code

Medication unique identifier.

start

Date of the medication delivery.

duration

Treatment duration of the delivery.

Statistic functions

Description

Determine the percentile from a qX value where X is a number from 1 to 3.

Usage

stat_quantile_prob(x)

Arguments

Value

Number 25, 50, 75

Examples

stat_quantile_prob("q1")
stat_quantile_prob("q2")
stat_quantile_prob("q3")

Utils

Description

Combination of sort() and unique().

Usage

sunique(x, decreasing = FALSE, na.last = FALSE)

Arguments

Value

x sorted and without duplicates.

Examples

sunique(c(1, 1, 1, 2, 2, NA, NA))
sunique(c(1, 1, 1, 2, 2, NA, NA), na.last = TRUE)
sunique(c(1, 1, 1, 2, 2, NA, NA), na.last = NA)
sunique(c(1, 1, 1, 2, 2, NA, NA), decreasing = TRUE)