ind_wcumul: Assess polypharmacy based on the number of distinct...

In polypharmacy: Calculate Several Polypharmacy Indicators

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

processed_tab	Table of individual drug treatments over the study period. Created by data_process function.
stats	Cohort descriptive statistics to calculate on the polypharmacy indicator. See Details for possible values.

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)

polypharmacy documentation built on July 12, 2021, 5:08 p.m.