Function for generating random Concomitant Medication Analysis Dataset for a given Subject-Level Analysis Dataset.
Arguments
- adsl
(
data.frame
)
Subject-Level Analysis Dataset (ADSL).- max_n_cms
(
integer
)
Maximum number of concomitant medications per patient. Defaults to 10.- lookup
(
data.frame
)
Additional parameters.- seed
(
numeric
)
Seed to use for reproducible random number generation.- na_percentage
(
proportion
)
Default percentage of values to be replaced byNA
.- na_vars
(
list
)
A named list where the name of each element is a column name ofds
. Each element of this list should be a numeric vector with two elements:seed
(numeric
)
The seed to be used for this element - can beNA
.percentage
(proportion
)
Percentage of elements to be replaced withNA
. IfNA
,na_percentage
is used as a default.
- who_coding
(
flag
)
Whether WHO coding (with multiple paths per medication) should be used.- cached
boolean whether the cached ADCM data
cadcm
should be returned or new data should be generated. If set toTRUE
then the other arguments toradcm
will be ignored.
Details
One record per each record in the corresponding SDTM domain.
Keys: STUDYID
, USUBJID
, ASTDTM
, CMSEQ
Examples
adsl <- radsl(N = 10, seed = 1, study_duration = 2)
adcm <- radcm(adsl, seed = 2)
adcm
#> # A tibble: 50 × 83
#> STUDYID USUBJID SUBJID SITEID AGE AGEU SEX RACE ETHNIC COUNTRY DTHFL
#> <chr> <chr> <chr> <chr> <int> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 AB12345 AB12345-B… id-8 BRA-9 31 YEARS F ASIAN NOT H… BRA Y
#> 2 AB12345 AB12345-B… id-8 BRA-9 31 YEARS F ASIAN NOT H… BRA Y
#> 3 AB12345 AB12345-B… id-8 BRA-9 31 YEARS F ASIAN NOT H… BRA Y
#> 4 AB12345 AB12345-B… id-8 BRA-9 31 YEARS F ASIAN NOT H… BRA Y
#> 5 AB12345 AB12345-B… id-8 BRA-9 31 YEARS F ASIAN NOT H… BRA Y
#> 6 AB12345 AB12345-B… id-8 BRA-9 31 YEARS F ASIAN NOT H… BRA Y
#> 7 AB12345 AB12345-C… id-1 CHN-11 35 YEARS F WHITE NOT H… CHN Y
#> 8 AB12345 AB12345-C… id-1 CHN-11 35 YEARS F WHITE NOT H… CHN Y
#> 9 AB12345 AB12345-C… id-1 CHN-11 35 YEARS F WHITE NOT H… CHN Y
#> 10 AB12345 AB12345-C… id-1 CHN-11 35 YEARS F WHITE NOT H… CHN Y
#> # ℹ 40 more rows
#> # ℹ 72 more variables: INVID <chr>, INVNAM <chr>, ARM <fct>, ARMCD <fct>,
#> # ACTARM <fct>, ACTARMCD <fct>, TRT01P <fct>, TRT01A <fct>, TRT02P <fct>,
#> # TRT02A <fct>, REGION1 <fct>, STRATA1 <fct>, STRATA2 <fct>, BMRKR1 <dbl>,
#> # BMRKR2 <fct>, ITTFL <fct>, SAFFL <fct>, BMEASIFL <fct>, BEP01FL <fct>,
#> # AEWITHFL <fct>, RANDDT <date>, TRTSDTM <dttm>, TRTEDTM <dttm>,
#> # TRT01SDTM <dttm>, TRT01EDTM <dttm>, TRT02SDTM <dttm>, TRT02EDTM <dttm>, …
adcm_who <- radcm(adsl, seed = 2, who_coding = TRUE)
adcm_who
#> # A tibble: 102 × 83
#> STUDYID USUBJID SUBJID SITEID AGE AGEU SEX RACE ETHNIC COUNTRY DTHFL
#> <chr> <chr> <chr> <chr> <int> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 AB12345 AB12345-B… id-8 BRA-9 31 YEARS F ASIAN NOT H… BRA Y
#> 2 AB12345 AB12345-B… id-8 BRA-9 31 YEARS F ASIAN NOT H… BRA Y
#> 3 AB12345 AB12345-B… id-8 BRA-9 31 YEARS F ASIAN NOT H… BRA Y
#> 4 AB12345 AB12345-B… id-8 BRA-9 31 YEARS F ASIAN NOT H… BRA Y
#> 5 AB12345 AB12345-B… id-8 BRA-9 31 YEARS F ASIAN NOT H… BRA Y
#> 6 AB12345 AB12345-B… id-8 BRA-9 31 YEARS F ASIAN NOT H… BRA Y
#> 7 AB12345 AB12345-C… id-1 CHN-11 35 YEARS F WHITE NOT H… CHN Y
#> 8 AB12345 AB12345-C… id-1 CHN-11 35 YEARS F WHITE NOT H… CHN Y
#> 9 AB12345 AB12345-C… id-1 CHN-11 35 YEARS F WHITE NOT H… CHN Y
#> 10 AB12345 AB12345-C… id-1 CHN-11 35 YEARS F WHITE NOT H… CHN Y
#> # ℹ 92 more rows
#> # ℹ 72 more variables: INVID <chr>, INVNAM <chr>, ARM <fct>, ARMCD <fct>,
#> # ACTARM <fct>, ACTARMCD <fct>, TRT01P <fct>, TRT01A <fct>, TRT02P <fct>,
#> # TRT02A <fct>, REGION1 <fct>, STRATA1 <fct>, STRATA2 <fct>, BMRKR1 <dbl>,
#> # BMRKR2 <fct>, ITTFL <fct>, SAFFL <fct>, BMEASIFL <fct>, BEP01FL <fct>,
#> # AEWITHFL <fct>, RANDDT <date>, TRTSDTM <dttm>, TRTEDTM <dttm>,
#> # TRT01SDTM <dttm>, TRT01EDTM <dttm>, TRT02SDTM <dttm>, TRT02EDTM <dttm>, …