The function adds subperiod, period, or phase variables like P01S1SDT
,
P01S2SDT
, AP01SDTM
, AP02SDTM
, TRT01A
, TRT02A
, PH1SDT
, PH2SDT
,
... to the input dataset. The values of the variables are defined by a period
reference dataset which has one observations per patient and subperiod,
period, or phase.
Usage
derive_vars_period(
dataset,
dataset_ref,
new_vars,
subject_keys = get_admiral_option("subject_keys")
)
Arguments
- dataset
ADSL dataset
The variables specified by
subject_keys
are expected.- dataset_ref
Period reference dataset
The variables specified by
new_vars
andsubject_keys
are expected.If subperiod variables are requested,
APERIOD
andASPER
are expected. If period variables are requested.APERIOD
is expected. If phase variables are requested,APHASEN
is expected.- new_vars
New variables
A named list of variables like
exprs(PHwSDT = PHSDT, PHwEDT = PHEDT, APHASEw = APHASE)
is expected. The left hand side of the elements defines a set of variables (in CDISC notation) to be added to the output dataset. The right hand side defines the source variable from the period reference dataset.If the lower case letter "w" is used it refers to a phase variable, if the lower case letters "xx" are used it refers to a period variable, and if both "xx" and "w" are used it refers to a subperiod variable.
Only one type must be used, e.g., all left hand side values must refer to period variables. It is not allowed to mix for example period and subperiod variables. If period and subperiod variables are required, separate calls must be used.
- subject_keys
Variables to uniquely identify a subject
A list of expressions where the expressions are symbols as returned by
exprs()
is expected.
Details
For each subperiod/period/phase in the period reference dataset and
each element in new_vars
a variable (LHS value of new_vars
) is added to
the output dataset and set to the value of the source variable (RHS value
of new_vars
.
See also
ADSL Functions that returns variable appended to dataset:
derive_var_age_years()
,
derive_var_dthcaus()
,
derive_var_extreme_dtm()
,
derive_var_extreme_dt()
,
derive_vars_aage()
Examples
library(tibble)
library(dplyr, warn.conflicts = FALSE)
library(lubridate)
adsl <- tibble(STUDYID = "xyz", USUBJID = c("1", "2"))
# Add period variables to ADSL
period_ref <- tribble(
~USUBJID, ~APERIOD, ~APERSDT, ~APEREDT,
"1", 1, "2021-01-04", "2021-02-06",
"1", 2, "2021-02-07", "2021-03-07",
"2", 1, "2021-02-02", "2021-03-02",
"2", 2, "2021-03-03", "2021-04-01"
) %>%
mutate(
STUDYID = "xyz",
APERIOD = as.integer(APERIOD),
across(matches("APER[ES]DT"), ymd)
)
derive_vars_period(
adsl,
dataset_ref = period_ref,
new_vars = exprs(APxxSDT = APERSDT, APxxEDT = APEREDT)
) %>%
select(STUDYID, USUBJID, AP01SDT, AP01EDT, AP02SDT, AP02EDT)
#> # A tibble: 2 x 6
#> STUDYID USUBJID AP01SDT AP01EDT AP02SDT AP02EDT
#> <chr> <chr> <date> <date> <date> <date>
#> 1 xyz 1 2021-01-04 2021-02-06 2021-02-07 2021-03-07
#> 2 xyz 2 2021-02-02 2021-03-02 2021-03-03 2021-04-01
# Add phase variables to ADSL
phase_ref <- tribble(
~USUBJID, ~APHASEN, ~PHSDT, ~PHEDT, ~APHASE,
"1", 1, "2021-01-04", "2021-02-06", "TREATMENT",
"1", 2, "2021-02-07", "2021-03-07", "FUP",
"2", 1, "2021-02-02", "2021-03-02", "TREATMENT"
) %>%
mutate(
STUDYID = "xyz",
APHASEN = as.integer(APHASEN),
across(matches("PH[ES]DT"), ymd)
)
derive_vars_period(
adsl,
dataset_ref = phase_ref,
new_vars = exprs(PHwSDT = PHSDT, PHwEDT = PHEDT, APHASEw = APHASE)
) %>%
select(STUDYID, USUBJID, PH1SDT, PH1EDT, PH2SDT, PH2EDT, APHASE1, APHASE2)
#> # A tibble: 2 x 8
#> STUDYID USUBJID PH1SDT PH1EDT PH2SDT PH2EDT APHASE1 APHASE2
#> <chr> <chr> <date> <date> <date> <date> <chr> <chr>
#> 1 xyz 1 2021-01-04 2021-02-06 2021-02-07 2021-03-07 TREATMENT FUP
#> 2 xyz 2 2021-02-02 2021-03-02 NA NA TREATMENT NA
# Add subperiod variables to ADSL
subperiod_ref <- tribble(
~USUBJID, ~APERIOD, ~ASPER, ~ASPRSDT, ~ASPREDT,
"1", 1, 1, "2021-01-04", "2021-01-19",
"1", 1, 2, "2021-01-20", "2021-02-06",
"1", 2, 1, "2021-02-07", "2021-03-07",
"2", 1, 1, "2021-02-02", "2021-03-02",
"2", 2, 1, "2021-03-03", "2021-04-01"
) %>%
mutate(
STUDYID = "xyz",
APERIOD = as.integer(APERIOD),
ASPER = as.integer(ASPER),
across(matches("ASPR[ES]DT"), ymd)
)
derive_vars_period(
adsl,
dataset_ref = subperiod_ref,
new_vars = exprs(PxxSwSDT = ASPRSDT, PxxSwEDT = ASPREDT)
) %>%
select(STUDYID, USUBJID, P01S1SDT, P01S1EDT, P01S2SDT, P01S2EDT, P02S1SDT, P02S1EDT)
#> # A tibble: 2 x 8
#> STUDYID USUBJID P01S1SDT P01S1EDT P01S2SDT P01S2EDT P02S1SDT
#> <chr> <chr> <date> <date> <date> <date> <date>
#> 1 xyz 1 2021-01-04 2021-01-19 2021-01-20 2021-02-06 2021-02-07
#> 2 xyz 2 2021-02-02 2021-03-02 NA NA 2021-03-03
#> # … with 1 more variable: P02S1EDT <date>