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This article describes creating a BDS finding ADaM. Examples are currently presented and tested in the context of ADVS. However, the examples could be applied to other BDS Finding ADaMs such as ADEG, ADLB, etc. where a single result is captured in an SDTM Finding domain on a single date and/or time.
Note: All examples assume CDISC SDTM and/or ADaM format as input unless otherwise specified.
ADT
, ADTM
, ADY
,
ADTF
, ATMF
)PARAMCD
, PARAM
,
PARAMN
, PARCAT1
AVAL
,
AVALC
)BSA
, BMI
, or MAP
for
ADVS
)APHASE
,
AVISIT
, APERIOD
)ONTRTFL
)ANRIND
)BASETYPE
,
ABLFL
, BASE
, BASEC
,
BNRIND
)CHG
,
PCHG
)SHIFT1
)R2BASE
)ANL01FL
)TRTA
,
TRTP
)ASEQ
AVALCATx
)To start, all data frames needed for the creation of
ADVS
should be read into the environment. This will be a
company specific process. Some of the data frames needed may be
VS
and ADSL
.
For example purpose, the CDISC Pilot SDTM and ADaM datasets—which are
included in {pharmaversesdtm}
—are used.
library(admiral)
library(dplyr, warn.conflicts = FALSE)
library(pharmaversesdtm)
library(lubridate)
library(stringr)
library(tibble)
data("admiral_adsl")
data("vs")
adsl <- admiral_adsl
vs <- convert_blanks_to_na(vs)
At this step, it may be useful to join ADSL
to your
VS
domain. Only the ADSL
variables used for
derivations are selected at this step. The rest of the relevant
ADSL
variables would be added later.
adsl_vars <- exprs(TRTSDT, TRTEDT, TRT01A, TRT01P)
advs <- derive_vars_merged(
vs,
dataset_add = adsl,
new_vars = adsl_vars,
by_vars = exprs(STUDYID, USUBJID)
)
USUBJID | VSTESTCD | VSDTC | VISIT | TRTSDT | TRTEDT | TRT01A | TRT01P |
---|---|---|---|---|---|---|---|
01-701-1015 | DIABP | 2014-01-16 | WEEK 2 | 2014-01-02 | 2014-07-02 | Placebo | Placebo |
01-701-1015 | DIABP | 2014-01-16 | WEEK 2 | 2014-01-02 | 2014-07-02 | Placebo | Placebo |
01-701-1015 | DIABP | 2014-01-16 | WEEK 2 | 2014-01-02 | 2014-07-02 | Placebo | Placebo |
01-701-1023 | DIABP | 2012-08-27 | WEEK 2 | 2012-08-05 | 2012-09-01 | Placebo | Placebo |
01-701-1023 | DIABP | 2012-08-27 | WEEK 2 | 2012-08-05 | 2012-09-01 | Placebo | Placebo |
01-701-1023 | DIABP | 2012-08-27 | WEEK 2 | 2012-08-05 | 2012-09-01 | Placebo | Placebo |
01-703-1086 | DIABP | 2012-09-16 | WEEK 2 | 2012-09-02 | 2012-12-04 | Xanomeline Low Dose | Xanomeline Low Dose |
01-703-1086 | DIABP | 2012-09-16 | WEEK 2 | 2012-09-02 | 2012-12-04 | Xanomeline Low Dose | Xanomeline Low Dose |
01-703-1086 | DIABP | 2012-09-16 | WEEK 2 | 2012-09-02 | 2012-12-04 | Xanomeline Low Dose | Xanomeline Low Dose |
01-703-1096 | DIABP | 2013-02-09 | WEEK 2 | 2013-01-25 | 2013-03-16 | Placebo | Placebo |
ADT
,
ADTM
, ADY
, ADTF
,
ATMF
)The function derive_vars_dt()
can be used to derive
ADT
. This function allows the user to impute the date as
well.
Example calls:
USUBJID | VISIT | VSDTC | ADT |
---|---|---|---|
01-701-1015 | SCREENING 1 | 2013-12-26 | 2013-12-26 |
01-701-1015 | SCREENING 1 | 2013-12-26 | 2013-12-26 |
01-701-1015 | SCREENING 1 | 2013-12-26 | 2013-12-26 |
01-701-1015 | SCREENING 2 | 2013-12-31 | 2013-12-31 |
01-701-1015 | SCREENING 2 | 2013-12-31 | 2013-12-31 |
01-701-1015 | SCREENING 2 | 2013-12-31 | 2013-12-31 |
01-701-1015 | BASELINE | 2014-01-02 | 2014-01-02 |
01-701-1015 | BASELINE | 2014-01-02 | 2014-01-02 |
01-701-1015 | BASELINE | 2014-01-02 | 2014-01-02 |
01-701-1015 | AMBUL ECG PLACEMENT | 2014-01-14 | 2014-01-14 |
If imputation is needed and the date is to be imputed to the first of the month, the call would be:
USUBJID | VISIT | VSDTC | ADT | ADTF |
---|---|---|---|---|
01-716-1024 | SCREENING 1 | 2012-07 | 2012-07-01 | D |
01-716-1024 | SCREENING 1 | 2012-07 | 2012-07-01 | D |
01-716-1024 | SCREENING 1 | 2012-07 | 2012-07-01 | D |
01-716-1024 | SCREENING 2 | 2012-07-07 | 2012-07-07 | NA |
01-716-1024 | SCREENING 2 | 2012-07-07 | 2012-07-07 | NA |
01-716-1024 | SCREENING 2 | 2012-07-07 | 2012-07-07 | NA |
01-716-1024 | BASELINE | 2012-07-09 | 2012-07-09 | NA |
01-716-1024 | BASELINE | 2012-07-09 | 2012-07-09 | NA |
01-716-1024 | BASELINE | 2012-07-09 | 2012-07-09 | NA |
01-716-1024 | AMBUL ECG PLACEMENT | 2012-07-22 | 2012-07-22 | NA |
Similarly, ADTM
may be created using the function
derive_vars_dtm()
. Imputation may be done on both the date
and time components of ADTM
.
# CDISC Pilot data does not contain times and the output of the derivation
# ADTM is not presented.
advs <- derive_vars_dtm(
advs,
new_vars_prefix = "A",
dtc = VSDTC,
highest_imputation = "M"
)
By default, the variable ADTF
for
derive_vars_dt()
or ADTF
and ATMF
for derive_vars_dtm()
will be created and populated with
the controlled terminology outlined in the ADaM IG for date
imputations.
See also Date and Time Imputation.
Once ADT
is derived, the function
derive_vars_dy()
can be used to derive ADY
.
This example assumes both ADT
and TRTSDT
exist
on the data frame.
USUBJID | VISIT | ADT | ADY | TRTSDT |
---|---|---|---|---|
01-716-1024 | SCREENING 1 | 2012-07-06 | -3 | 2012-07-09 |
01-716-1024 | SCREENING 1 | 2012-07-06 | -3 | 2012-07-09 |
01-716-1024 | SCREENING 1 | 2012-07-06 | -3 | 2012-07-09 |
01-716-1024 | SCREENING 2 | 2012-07-07 | -2 | 2012-07-09 |
01-716-1024 | SCREENING 2 | 2012-07-07 | -2 | 2012-07-09 |
01-716-1024 | SCREENING 2 | 2012-07-07 | -2 | 2012-07-09 |
01-716-1024 | BASELINE | 2012-07-09 | 1 | 2012-07-09 |
01-716-1024 | BASELINE | 2012-07-09 | 1 | 2012-07-09 |
01-716-1024 | BASELINE | 2012-07-09 | 1 | 2012-07-09 |
01-716-1024 | AMBUL ECG PLACEMENT | 2012-07-22 | 14 | 2012-07-09 |
PARAMCD
, PARAM
,
PARAMN
, PARCAT1
To assign parameter level values such as PARAMCD
,
PARAM
, PARAMN
, PARCAT1
, etc., a
lookup can be created to join to the source data.
For example, when creating ADVS
, a lookup based on the
SDTM --TESTCD
value may be created:
VSTESTCD |
PARAMCD |
PARAM |
PARAMN |
PARCAT1 |
PARCAT1N |
---|---|---|---|---|---|
HEIGHT | HEIGHT | Height (cm) | 1 | Subject Characteristic | 1 |
WEIGHT | WEIGHT | Weight (kg) | 2 | Subject Characteristic | 1 |
DIABP | DIABP | Diastolic Blood Pressure (mmHg) | 3 | Vital Sign | 2 |
MAP | MAP | Mean Arterial Pressure | 4 | Vital Sign | 2 |
PULSE | PULSE | Pulse Rate (beats/min) | 5 | Vital Sign | 2 |
SYSBP | SYSBP | Systolic Blood Pressure (mmHg) | 6 | Vital Sign | 2 |
TEMP | TEMP | Temperature (C) | 7 | Vital Sign | 2 |
This lookup may now be joined to the source data:
At this stage, only PARAMCD
is required to perform the
derivations. Additional derived parameters may be added, so only
PARAMCD
is joined to the datasets at this point. All other
variables related to PARAMCD
(e.g. PARAM
,
PARAMCAT1
, …) will be added when all PARAMCD
are derived.
advs <- derive_vars_merged_lookup(
advs,
dataset_add = param_lookup,
new_vars = exprs(PARAMCD),
by_vars = exprs(VSTESTCD)
)
#> All `VSTESTCD` are mapped.
USUBJID | VSTESTCD | PARAMCD |
---|---|---|
01-701-1015 | DIABP | DIABP |
01-701-1015 | HEIGHT | HEIGHT |
01-701-1015 | PULSE | PULSE |
01-701-1015 | SYSBP | SYSBP |
01-701-1015 | TEMP | TEMP |
01-701-1015 | WEIGHT | WEIGHT |
01-701-1023 | DIABP | DIABP |
01-701-1023 | HEIGHT | HEIGHT |
01-701-1023 | PULSE | PULSE |
01-701-1023 | SYSBP | SYSBP |
Please note, it may be necessary to include other variables in the
join. For example, perhaps the PARAMCD
is based on
VSTESTCD
and VSPOS
, it may be necessary to
expand this lookup or create a separate look up for
PARAMCD
.
If more than one lookup table, e.g., company parameter mappings and
project parameter mappings, are available,
consolidate_metadata()
can be used to consolidate these
into a single lookup table.
AVAL
, AVALC
)The mapping of AVAL
and AVALC
is left to
the ADaM programmer. An example mapping may be:
In this example, as is often the case for ADVS, all AVAL
values are numeric without any corresponding non-redundant text value
for AVALC
. Per recommendation in ADaMIG v1.3 we do not map
AVALC
.
VSTESTCD | PARAMCD | VSSTRESN | VSSTRESC | AVAL |
---|---|---|---|---|
DIABP | DIABP | 74 | 74 | 74 |
DIABP | DIABP | 74 | 74 | 74 |
DIABP | DIABP | 72 | 72 | 72 |
DIABP | DIABP | 78 | 78 | 78 |
DIABP | DIABP | 84 | 84 | 84 |
DIABP | DIABP | 78 | 78 | 78 |
DIABP | DIABP | 80 | 80 | 80 |
DIABP | DIABP | 80 | 80 | 80 |
DIABP | DIABP | 84 | 84 | 84 |
DIABP | DIABP | 90 | 90 | 90 |
BSA
,
BMI
or MAP
for ADVS
)Optionally derive new parameters creating PARAMCD
and
AVAL
. Note that only variables specified in the
by_vars
argument will be populated in the newly created
records. This is relevant to the functions
derive_param_map
, derive_param_bsa
,
derive_param_bmi
, and derive_param_qtc
.
Below is an example of creating Mean Arterial Pressure
for ADVS
, see also Example 3 in section below Derive New Rows for alternative way of creating
new parameters.
advs <- derive_param_map(
advs,
by_vars = exprs(STUDYID, USUBJID, !!!adsl_vars, VISIT, VISITNUM, ADT, ADY, VSTPT, VSTPTNUM),
set_values_to = exprs(PARAMCD = "MAP"),
get_unit_expr = VSSTRESU,
filter = VSSTAT != "NOT DONE" | is.na(VSSTAT)
)
VSTESTCD | PARAMCD | VISIT | VSTPT | AVAL |
---|---|---|---|---|
DIABP | DIABP | SCREENING 1 | AFTER LYING DOWN FOR 5 MINUTES | 64.00000 |
NA | MAP | SCREENING 1 | AFTER LYING DOWN FOR 5 MINUTES | 86.33333 |
SYSBP | SYSBP | SCREENING 1 | AFTER LYING DOWN FOR 5 MINUTES | 131.00000 |
DIABP | DIABP | SCREENING 1 | AFTER STANDING FOR 1 MINUTE | 83.00000 |
NA | MAP | SCREENING 1 | AFTER STANDING FOR 1 MINUTE | 98.33333 |
SYSBP | SYSBP | SCREENING 1 | AFTER STANDING FOR 1 MINUTE | 129.00000 |
DIABP | DIABP | SCREENING 1 | AFTER STANDING FOR 3 MINUTES | 57.00000 |
NA | MAP | SCREENING 1 | AFTER STANDING FOR 3 MINUTES | 87.00000 |
SYSBP | SYSBP | SCREENING 1 | AFTER STANDING FOR 3 MINUTES | 147.00000 |
DIABP | DIABP | SCREENING 2 | AFTER LYING DOWN FOR 5 MINUTES | 68.00000 |
Likewise, function call below, to create parameter
Body Surface Area
(BSA) and Body Mass Index
(BMI) for ADVS
domain. Note that if height is collected
only once use constant_by_vars
to specify the subject-level
variable to merge on. Otherwise BSA and BMI are only calculated for
visits where both are collected.
advs <- derive_param_bsa(
advs,
by_vars = exprs(STUDYID, USUBJID, !!!adsl_vars, VISIT, VISITNUM, ADT, ADY, VSTPT, VSTPTNUM),
method = "Mosteller",
set_values_to = exprs(PARAMCD = "BSA"),
get_unit_expr = VSSTRESU,
filter = VSSTAT != "NOT DONE" | is.na(VSSTAT),
constant_by_vars = exprs(USUBJID)
)
advs <- derive_param_bmi(
advs,
by_vars = exprs(STUDYID, USUBJID, !!!adsl_vars, VISIT, VISITNUM, ADT, ADY, VSTPT, VSTPTNUM),
set_values_to = exprs(PARAMCD = "BMI"),
get_unit_expr = VSSTRESU,
filter = VSSTAT != "NOT DONE" | is.na(VSSTAT),
constant_by_vars = exprs(USUBJID)
)
USUBJID | VSTESTCD | PARAMCD | VISIT | VSTPT | AVAL |
---|---|---|---|---|---|
01-701-1015 | NA | BMI | SCREENING 1 | NA | 24.871928 |
01-701-1015 | NA | BSA | SCREENING 1 | NA | 1.486264 |
01-701-1015 | NA | BMI | BASELINE | NA | 25.079271 |
01-701-1015 | NA | BSA | BASELINE | NA | 1.492447 |
01-701-1015 | NA | BMI | WEEK 2 | NA | 24.452635 |
01-701-1015 | NA | BSA | WEEK 2 | NA | 1.473683 |
01-701-1015 | NA | BMI | WEEK 4 | NA | 24.871928 |
01-701-1015 | NA | BSA | WEEK 4 | NA | 1.486264 |
01-701-1015 | NA | BMI | WEEK 6 | NA | 24.452635 |
01-701-1015 | NA | BSA | WEEK 6 | NA | 1.473683 |
Similarly, for ADEG
, the parameters QTCBF
QTCBS
and QTCL
can be created with a function
call. See example below for PARAMCD
=
QTCF
.
adeg <- tibble::tribble(
~USUBJID, ~EGSTRESU, ~PARAMCD, ~AVAL, ~VISIT,
"P01", "msec", "QT", 350, "CYCLE 1 DAY 1",
"P01", "msec", "QT", 370, "CYCLE 2 DAY 1",
"P01", "msec", "RR", 842, "CYCLE 1 DAY 1",
"P01", "msec", "RR", 710, "CYCLE 2 DAY 1"
)
adeg <- derive_param_qtc(
adeg,
by_vars = exprs(USUBJID, VISIT),
method = "Fridericia",
set_values_to = exprs(PARAMCD = "QTCFR"),
get_unit_expr = EGSTRESU
)
Similarly, for ADLB
, the function
derive_param_wbc_abs()
can be used to create new parameter
for lab differentials converted to absolute values. See example
below:
adlb <- tibble::tribble(
~USUBJID, ~PARAMCD, ~AVAL, ~PARAM, ~VISIT,
"P01", "WBC", 33, "Leukocyte Count (10^9/L)", "CYCLE 1 DAY 1",
"P01", "WBC", 38, "Leukocyte Count (10^9/L)", "CYCLE 2 DAY 1",
"P01", "LYMLE", 0.90, "Lymphocytes (fraction of 1)", "CYCLE 1 DAY 1",
"P01", "LYMLE", 0.70, "Lymphocytes (fraction of 1)", "CYCLE 2 DAY 1"
)
derive_param_wbc_abs(
dataset = adlb,
by_vars = exprs(USUBJID, VISIT),
set_values_to = exprs(
PARAMCD = "LYMPH",
PARAM = "Lymphocytes Abs (10^9/L)",
DTYPE = "CALCULATION"
),
get_unit_expr = extract_unit(PARAM),
wbc_code = "WBC",
diff_code = "LYMLE",
diff_type = "fraction"
)
When all PARAMCD
have been derived and added to the
dataset, the other information from the look-up table
(PARAM
, PARAMCAT1
,…) should be added.
# Derive PARAM and PARAMN
advs <- derive_vars_merged(
advs,
dataset_add = select(param_lookup, -VSTESTCD),
by_vars = exprs(PARAMCD)
)
VSTESTCD | PARAMCD | PARAM | PARAMN | PARCAT1 | PARCAT1N |
---|---|---|---|---|---|
DIABP | DIABP | Diastolic Blood Pressure (mmHg) | 3 | Vital Sign | 2 |
DIABP | DIABP | Diastolic Blood Pressure (mmHg) | 3 | Vital Sign | 2 |
DIABP | DIABP | Diastolic Blood Pressure (mmHg) | 3 | Vital Sign | 2 |
DIABP | DIABP | Diastolic Blood Pressure (mmHg) | 3 | Vital Sign | 2 |
DIABP | DIABP | Diastolic Blood Pressure (mmHg) | 3 | Vital Sign | 2 |
DIABP | DIABP | Diastolic Blood Pressure (mmHg) | 3 | Vital Sign | 2 |
DIABP | DIABP | Diastolic Blood Pressure (mmHg) | 3 | Vital Sign | 2 |
DIABP | DIABP | Diastolic Blood Pressure (mmHg) | 3 | Vital Sign | 2 |
DIABP | DIABP | Diastolic Blood Pressure (mmHg) | 3 | Vital Sign | 2 |
DIABP | DIABP | Diastolic Blood Pressure (mmHg) | 3 | Vital Sign | 2 |
APHASE
,
AVISIT
, APERIOD
)Categorical timing variables are protocol and analysis dependent. Below is a simple example.
advs <- advs %>%
mutate(
AVISIT = case_when(
str_detect(VISIT, "SCREEN") ~ NA_character_,
str_detect(VISIT, "UNSCHED") ~ NA_character_,
str_detect(VISIT, "RETRIEVAL") ~ NA_character_,
str_detect(VISIT, "AMBUL") ~ NA_character_,
!is.na(VISIT) ~ str_to_title(VISIT)
),
AVISITN = as.numeric(case_when(
VISIT == "BASELINE" ~ "0",
str_detect(VISIT, "WEEK") ~ str_trim(str_replace(VISIT, "WEEK", ""))
)),
ATPT = VSTPT,
ATPTN = VSTPTNUM
)
count(advs, VISITNUM, VISIT, AVISITN, AVISIT)
#> # A tibble: 15 × 5
#> VISITNUM VISIT AVISITN AVISIT n
#> <dbl> <chr> <dbl> <chr> <int>
#> 1 1 SCREENING 1 NA <NA> 102
#> 2 2 SCREENING 2 NA <NA> 78
#> 3 3 BASELINE 0 Baseline 96
#> 4 3.5 AMBUL ECG PLACEMENT NA <NA> 65
#> 5 4 WEEK 2 2 Week 2 96
#> 6 5 WEEK 4 4 Week 4 80
#> 7 6 AMBUL ECG REMOVAL NA <NA> 52
#> 8 7 WEEK 6 6 Week 6 48
#> 9 8 WEEK 8 8 Week 8 48
#> 10 9 WEEK 12 12 Week 12 48
#> 11 10 WEEK 16 16 Week 16 48
#> 12 11 WEEK 20 20 Week 20 32
#> 13 12 WEEK 24 24 Week 24 32
#> 14 13 WEEK 26 26 Week 26 32
#> 15 201 RETRIEVAL NA <NA> 26
count(advs, VSTPTNUM, VSTPT, ATPTN, ATPT)
#> # A tibble: 4 × 5
#> VSTPTNUM VSTPT ATPTN ATPT n
#> <dbl> <chr> <dbl> <chr> <int>
#> 1 815 AFTER LYING DOWN FOR 5 MINUTES 815 AFTER LYING DOWN FOR 5 MI… 232
#> 2 816 AFTER STANDING FOR 1 MINUTE 816 AFTER STANDING FOR 1 MINU… 232
#> 3 817 AFTER STANDING FOR 3 MINUTES 817 AFTER STANDING FOR 3 MINU… 232
#> 4 NA <NA> NA <NA> 187
For assigning visits based on time windows and deriving periods, subperiods, and phase variables see the “Visit and Period Variables” vignette.
ONTRTFL
)In some analyses, it may be necessary to flag an observation as
on-treatment. The admiral function derive_var_ontrtfl()
can
be used.
For example, if on-treatment is defined as any observation between treatment start and treatment end, the flag may be derived as:
advs <- derive_var_ontrtfl(
advs,
start_date = ADT,
ref_start_date = TRTSDT,
ref_end_date = TRTEDT
)
USUBJID | PARAMCD | ADT | TRTSDT | TRTEDT | ONTRTFL |
---|---|---|---|---|---|
01-701-1015 | DIABP | 2014-01-16 | 2014-01-02 | 2014-07-02 | Y |
01-701-1015 | DIABP | 2014-01-16 | 2014-01-02 | 2014-07-02 | Y |
01-701-1015 | DIABP | 2014-01-16 | 2014-01-02 | 2014-07-02 | Y |
01-701-1023 | DIABP | 2012-08-27 | 2012-08-05 | 2012-09-01 | Y |
01-701-1023 | DIABP | 2012-08-27 | 2012-08-05 | 2012-09-01 | Y |
01-701-1023 | DIABP | 2012-08-27 | 2012-08-05 | 2012-09-01 | Y |
01-703-1086 | DIABP | 2012-09-16 | 2012-09-02 | 2012-12-04 | Y |
01-703-1086 | DIABP | 2012-09-16 | 2012-09-02 | 2012-12-04 | Y |
01-703-1086 | DIABP | 2012-09-16 | 2012-09-02 | 2012-12-04 | Y |
01-703-1096 | DIABP | 2013-02-09 | 2013-01-25 | 2013-03-16 | Y |
This function returns the original data frame with the column
ONTRTFL
added. Additionally, this function does have
functionality to handle a window on the ref_end_date
. For
example, if on-treatment is defined as between treatment start and
treatment end plus 60 days, the call would be:
advs <- derive_var_ontrtfl(
advs,
start_date = ADT,
ref_start_date = TRTSDT,
ref_end_date = TRTEDT,
ref_end_window = 60
)
In addition, the function does allow you to filter out pre-treatment
observations that occurred on the start date. For example, if
observations with VSTPT == PRE
should not be considered
on-treatment when the observation date falls between the treatment start
and end date, the user may specify this using the
filter_pre_timepoint
parameter:
advs <- derive_var_ontrtfl(
advs,
start_date = ADT,
ref_start_date = TRTSDT,
ref_end_date = TRTEDT,
filter_pre_timepoint = ATPT == "AFTER LYING DOWN FOR 5 MINUTES"
)
Lastly, the function does allow you to create any on-treatment flag
based on the analysis needs. For example, if variable
ONTR01FL
is needed, showing the on-treatment flag during
Period 01, you need to set new var = ONTR01FL
. In addition,
for Period 01 Start Date and Period 01 End Date, you need
ref_start_date = AP01SDT
and
ref_end_date = AP01EDT
.
advs <- derive_var_ontrtfl(
advs,
new_var = ONTR01FL,
start_date = ASTDT,
end_date = AENDT,
ref_start_date = AP01SDT,
ref_end_date = AP01EDT,
span_period = TRUE
)
USUBJID | ASTDT | AENDT | AP01SDT | AP01EDT | ONTR01FL |
---|---|---|---|---|---|
P01 | 2020-03-15 | 2020-12-01 | 2020-01-01 | 2020-03-01 | NA |
P02 | 2019-04-30 | 2020-03-15 | 2020-01-01 | 2020-03-01 | Y |
P03 | 2019-04-30 | NA | 2020-01-01 | 2020-03-01 | Y |
ANRIND
)The admiral function derive_var_anrind()
may be used to
derive the reference range indicator ANRIND
.
This function requires the reference range boundaries to exist on the
data frame (ANRLO
, ANRHI
) and also
accommodates the additional boundaries A1LO
and
A1HI
.
The function is called as:
USUBJID | PARAMCD | AVAL | ANRLO | ANRHI | A1LO | A1HI | ANRIND |
---|---|---|---|---|---|---|---|
01-701-1015 | DIABP | 56 | 60 | 80 | 40 | 90 | LOW |
01-701-1015 | DIABP | 50 | 60 | 80 | 40 | 90 | LOW |
01-701-1015 | DIABP | 54 | 60 | 80 | 40 | 90 | LOW |
01-701-1023 | DIABP | 88 | 60 | 80 | 40 | 90 | HIGH |
01-701-1023 | DIABP | 86 | 60 | 80 | 40 | 90 | HIGH |
01-701-1023 | DIABP | 90 | 60 | 80 | 40 | 90 | HIGH |
01-703-1086 | DIABP | 68 | 60 | 80 | 40 | 90 | NORMAL |
01-703-1086 | DIABP | 74 | 60 | 80 | 40 | 90 | NORMAL |
01-703-1086 | DIABP | 70 | 60 | 80 | 40 | 90 | NORMAL |
01-703-1096 | DIABP | 74 | 60 | 80 | 40 | 90 | NORMAL |
BASETYPE
, ABLFL
,
BASE
, BNRIND
)The BASETYPE
should be derived using the function
derive_basetype_records()
. The parameter
basetypes
of this function requires a named list of
expression detailing how the BASETYPE
should be assigned.
Note, if a record falls into multiple expressions within the basetypes
expression, a row will be produced for each BASETYPE
.
advs <- derive_basetype_records(
dataset = advs,
basetypes = exprs(
"LAST: AFTER LYING DOWN FOR 5 MINUTES" = ATPTN == 815,
"LAST: AFTER STANDING FOR 1 MINUTE" = ATPTN == 816,
"LAST: AFTER STANDING FOR 3 MINUTES" = ATPTN == 817,
"LAST" = is.na(ATPTN)
)
)
count(advs, ATPT, ATPTN, BASETYPE)
#> # A tibble: 4 × 4
#> ATPT ATPTN BASETYPE n
#> <chr> <dbl> <chr> <int>
#> 1 AFTER LYING DOWN FOR 5 MINUTES 815 LAST: AFTER LYING DOWN FOR 5 MINUT… 232
#> 2 AFTER STANDING FOR 1 MINUTE 816 LAST: AFTER STANDING FOR 1 MINUTE 232
#> 3 AFTER STANDING FOR 3 MINUTES 817 LAST: AFTER STANDING FOR 3 MINUTES 232
#> 4 <NA> NA LAST 187
It is important to derive BASETYPE
first so that it can
be utilized in subsequent derivations. This will be important if the
data frame contains multiple values for BASETYPE
.
Next, the analysis baseline flag ABLFL
can be derived
using the {admiral}
function
derive_var_extreme_flag()
. For example, if baseline is
defined as the last non-missing AVAL
prior or on
TRTSDT
, the function call for ABLFL
would
be:
advs <- restrict_derivation(
advs,
derivation = derive_var_extreme_flag,
args = params(
by_vars = exprs(STUDYID, USUBJID, BASETYPE, PARAMCD),
order = exprs(ADT, ATPTN, VISITNUM),
new_var = ABLFL,
mode = "last"
),
filter = (!is.na(AVAL) & ADT <= TRTSDT & !is.na(BASETYPE))
)
USUBJID | BASETYPE | PARAMCD | ADT | TRTSDT | ATPTN | ABLFL |
---|---|---|---|---|---|---|
01-701-1015 | LAST: AFTER LYING DOWN FOR 5 MINUTES | DIABP | 2014-01-02 | 2014-01-02 | 815 | Y |
01-701-1015 | LAST: AFTER STANDING FOR 1 MINUTE | DIABP | 2014-01-02 | 2014-01-02 | 816 | Y |
01-701-1015 | LAST: AFTER STANDING FOR 3 MINUTES | DIABP | 2014-01-02 | 2014-01-02 | 817 | Y |
01-701-1023 | LAST: AFTER LYING DOWN FOR 5 MINUTES | DIABP | 2012-08-05 | 2012-08-05 | 815 | Y |
01-701-1023 | LAST: AFTER STANDING FOR 1 MINUTE | DIABP | 2012-08-05 | 2012-08-05 | 816 | Y |
01-701-1023 | LAST: AFTER STANDING FOR 3 MINUTES | DIABP | 2012-08-05 | 2012-08-05 | 817 | Y |
01-703-1086 | LAST: AFTER LYING DOWN FOR 5 MINUTES | DIABP | 2012-09-02 | 2012-09-02 | 815 | Y |
01-703-1086 | LAST: AFTER STANDING FOR 1 MINUTE | DIABP | 2012-09-02 | 2012-09-02 | 816 | Y |
01-703-1086 | LAST: AFTER STANDING FOR 3 MINUTES | DIABP | 2012-09-02 | 2012-09-02 | 817 | Y |
01-703-1096 | LAST: AFTER LYING DOWN FOR 5 MINUTES | DIABP | 2013-01-25 | 2013-01-25 | 815 | Y |
Note: Additional examples of the
derive_var_extreme_flag()
function can be found above.
Lastly, the BASE
, and BNRIND
columns can be
derived using the {admiral}
function
derive_var_base()
. Example calls are:
advs <- derive_var_base(
advs,
by_vars = exprs(STUDYID, USUBJID, PARAMCD, BASETYPE),
source_var = AVAL,
new_var = BASE
)
advs <- derive_var_base(
advs,
by_vars = exprs(STUDYID, USUBJID, PARAMCD, BASETYPE),
source_var = ANRIND,
new_var = BNRIND
)
USUBJID | BASETYPE | PARAMCD | ABLFL | BASE | ANRIND | BNRIND |
---|---|---|---|---|---|---|
01-701-1015 | LAST: AFTER LYING DOWN FOR 5 MINUTES | DIABP | Y | 56 | LOW | LOW |
01-701-1015 | LAST: AFTER STANDING FOR 1 MINUTE | DIABP | Y | 51 | LOW | LOW |
01-701-1015 | LAST: AFTER STANDING FOR 3 MINUTES | DIABP | Y | 61 | NORMAL | NORMAL |
01-701-1023 | LAST: AFTER LYING DOWN FOR 5 MINUTES | DIABP | Y | 84 | HIGH | HIGH |
01-701-1023 | LAST: AFTER STANDING FOR 1 MINUTE | DIABP | Y | 86 | HIGH | HIGH |
01-701-1023 | LAST: AFTER STANDING FOR 3 MINUTES | DIABP | Y | 88 | HIGH | HIGH |
01-703-1086 | LAST: AFTER LYING DOWN FOR 5 MINUTES | DIABP | Y | 80 | NORMAL | NORMAL |
01-703-1086 | LAST: AFTER STANDING FOR 1 MINUTE | DIABP | Y | 82 | HIGH | HIGH |
01-703-1086 | LAST: AFTER STANDING FOR 3 MINUTES | DIABP | Y | 72 | NORMAL | NORMAL |
01-703-1096 | LAST: AFTER LYING DOWN FOR 5 MINUTES | DIABP | Y | 70 | NORMAL | NORMAL |
CHG
,
PCHG
)Change and percent change from baseline can be derived using the
{admiral}
functions derive_var_chg()
and
derive_var_pchg()
. These functions expect AVAL
and BASE
to exist in the data frame. The CHG
is simply AVAL - BASE
and the PCHG
is
(AVAL - BASE) / absolute value (BASE) * 100
. Examples calls
are:
USUBJID | VISIT | BASE | AVAL | CHG | PCHG |
---|---|---|---|---|---|
01-701-1015 | WEEK 2 | 56 | 56 | 0 | 0.000000 |
01-701-1015 | WEEK 8 | 56 | 67 | 11 | 19.642857 |
01-701-1023 | WEEK 2 | 84 | 88 | 4 | 4.761905 |
01-703-1086 | WEEK 2 | 80 | 68 | -12 | -15.000000 |
01-703-1086 | WEEK 8 | 80 | 80 | 0 | 0.000000 |
01-703-1096 | WEEK 2 | 70 | 74 | 4 | 5.714286 |
01-707-1037 | WEEK 2 | 88 | 72 | -16 | -18.181818 |
01-716-1024 | WEEK 2 | 80 | 86 | 6 | 7.500000 |
01-716-1024 | WEEK 8 | 80 | 78 | -2 | -2.500000 |
01-701-1015 | WEEK 2 | 51 | 50 | -1 | -1.960784 |
If the variables should not be derived for all records, e.g., for
post-baseline records only, restrict_derivation()
can be
used.
SHIFT1
)Shift variables can be derived using the {admiral}
function derive_var_shift()
. This function derives a
character shift variable concatenating shift in values based on a
user-defined pairing, e.g., shift from baseline reference range
BNRIND
to analysis reference range ANRIND
.
Examples calls are:
If the variables should not be derived for all records, e.g., for
post-baseline records only, restrict_derivation()
can be
used.
R2BASE
)Analysis ratio variables can be derived using the
{admiral}
function
derive_var_analysis_ratio()
. This function derives a ratio
variable based on user-specified pair. For example, Ratio to Baseline is
calculated by AVAL / BASE
and the function appends a new
variable R2BASE
to the dataset. Examples calls are:
advs <- derive_var_analysis_ratio(advs,
numer_var = AVAL,
denom_var = BASE
)
advs <- derive_var_analysis_ratio(advs,
numer_var = AVAL,
denom_var = ANRLO,
new_var = R01ANRLO
)
USUBJID | VISIT | BASE | AVAL | ANRLO | R2BASE | R01ANRLO |
---|---|---|---|---|---|---|
01-701-1015 | WEEK 2 | 56 | 56 | 60 | 1.0000000 | 0.9333333 |
01-701-1015 | WEEK 8 | 56 | 67 | 60 | 1.1964286 | 1.1166667 |
01-701-1023 | WEEK 2 | 84 | 88 | 60 | 1.0476190 | 1.4666667 |
01-703-1086 | WEEK 2 | 80 | 68 | 60 | 0.8500000 | 1.1333333 |
01-703-1086 | WEEK 8 | 80 | 80 | 60 | 1.0000000 | 1.3333333 |
01-703-1096 | WEEK 2 | 70 | 74 | 60 | 1.0571429 | 1.2333333 |
01-707-1037 | WEEK 2 | 88 | 72 | 60 | 0.8181818 | 1.2000000 |
01-716-1024 | WEEK 2 | 80 | 86 | 60 | 1.0750000 | 1.4333333 |
01-716-1024 | WEEK 8 | 80 | 78 | 60 | 0.9750000 | 1.3000000 |
01-701-1015 | WEEK 2 | 51 | 50 | 60 | 0.9803922 | 0.8333333 |
If the variables should not be derived for all records, e.g., for
post-baseline records only, restrict_derivation()
can be
used.
ANL01FL
)In most finding ADaMs, an analysis flag is derived to identify the appropriate observation(s) to use for a particular analysis when a subject has multiple observations within a particular timing period.
In this situation, an analysis flag (e.g. ANLxxFL
) may
be used to choose the appropriate record for analysis.
This flag may be derived using the {admiral}
function
derive_var_extreme_flag()
. For this example, we will assume
we would like to choose the latest and highest value by
USUBJID
, PARAMCD
, AVISIT
, and
ATPT
.
advs <- restrict_derivation(
advs,
derivation = derive_var_extreme_flag,
args = params(
by_vars = exprs(STUDYID, USUBJID, BASETYPE, PARAMCD, AVISIT),
order = exprs(ADT, ATPTN, AVAL),
new_var = ANL01FL,
mode = "last"
),
filter = !is.na(AVISITN)
)
USUBJID | PARAMCD | AVISIT | ATPTN | ADT | AVAL | ANL01FL |
---|---|---|---|---|---|---|
01-701-1015 | DIABP | Week 2 | 815 | 2014-01-16 | 56 | Y |
01-701-1015 | DIABP | Week 8 | 815 | 2014-03-05 | 67 | Y |
01-701-1015 | DIABP | Week 2 | 816 | 2014-01-16 | 50 | Y |
01-701-1015 | DIABP | Week 8 | 816 | 2014-03-05 | 62 | Y |
01-701-1015 | DIABP | Week 2 | 817 | 2014-01-16 | 54 | Y |
01-701-1015 | DIABP | Week 8 | 817 | 2014-03-05 | 71 | Y |
01-701-1023 | DIABP | Week 2 | 815 | 2012-08-27 | 88 | Y |
01-701-1023 | DIABP | Week 2 | 816 | 2012-08-27 | 86 | Y |
01-701-1023 | DIABP | Week 2 | 817 | 2012-08-27 | 90 | Y |
01-703-1086 | DIABP | Week 2 | 815 | 2012-09-16 | 68 | Y |
Another common example would be flagging the worst value for a
subject, parameter, and visit. For this example, we will assume we have
3 PARAMCD
values (SYSBP
, DIABP
,
and RESP
). We will also assume high is worst for
SYSBP
and DIABP
and low is worst for
RESP
.
advs <- slice_derivation(
advs,
derivation = derive_var_extreme_flag,
args = params(
by_vars = exprs(STUDYID, USUBJID, BASETYPE, PARAMCD, AVISIT),
order = exprs(ADT, ATPTN),
new_var = WORSTFL,
mode = "first"
),
derivation_slice(
filter = PARAMCD %in% c("SYSBP", "DIABP") & (!is.na(AVISIT) & !is.na(AVAL))
),
derivation_slice(
filter = PARAMCD %in% "PULSE" & (!is.na(AVISIT) & !is.na(AVAL)),
args = params(mode = "last")
)
) %>%
arrange(STUDYID, USUBJID, BASETYPE, PARAMCD, AVISIT)
USUBJID | PARAMCD | AVISIT | AVAL | ADT | ATPTN | WORSTFL |
---|---|---|---|---|---|---|
01-701-1015 | DIABP | Baseline | 56 | 2014-01-02 | 815 | Y |
01-701-1015 | DIABP | Week 12 | 68 | 2014-03-26 | 815 | Y |
01-701-1015 | DIABP | Week 16 | 66 | 2014-05-07 | 815 | Y |
01-701-1015 | DIABP | Week 2 | 56 | 2014-01-16 | 815 | Y |
01-701-1015 | DIABP | Week 20 | 67 | 2014-05-21 | 815 | Y |
01-701-1015 | DIABP | Week 24 | 63 | 2014-06-18 | 815 | Y |
01-701-1015 | DIABP | Week 26 | 61 | 2014-07-02 | 815 | Y |
01-701-1015 | DIABP | Week 4 | 64 | 2014-01-30 | 815 | Y |
01-701-1015 | DIABP | Week 6 | 55 | 2014-02-12 | 815 | Y |
01-701-1015 | DIABP | Week 8 | 67 | 2014-03-05 | 815 | Y |
TRTA
, TRTP
)TRTA
and TRTP
must match at least one value
of the character treatment variables in ADSL (e.g.,
TRTxxA
/TRTxxP
,
TRTSEQA
/TRTSEQP
,
TRxxAGy
/TRxxPGy
).
An example of a simple implementation for a study without periods could be:
advs <- mutate(advs, TRTP = TRT01P, TRTA = TRT01A)
count(advs, TRTP, TRTA, TRT01P, TRT01A)
#> # A tibble: 2 × 5
#> TRTP TRTA TRT01P TRT01A n
#> <chr> <chr> <chr> <chr> <int>
#> 1 Placebo Placebo Placebo Placebo 640
#> 2 Xanomeline Low Dose Xanomeline Low Dose Xanomeline Low Dose Xanomeline … 243
For studies with periods see the “Visit and Period Variables” vignette.
ASEQ
The {admiral}
function
derive_var_obs_number()
can be used to derive
ASEQ
. An example call is:
advs <- derive_var_obs_number(
advs,
new_var = ASEQ,
by_vars = exprs(STUDYID, USUBJID),
order = exprs(PARAMCD, ADT, AVISITN, VISITNUM, ATPTN),
check_type = "error"
)
USUBJID | PARAMCD | ADT | AVISITN | ATPTN | VISIT | ASEQ |
---|---|---|---|---|---|---|
01-701-1015 | BMI | 2013-12-26 | NA | NA | SCREENING 1 | 1 |
01-701-1015 | BMI | 2014-01-02 | 0 | NA | BASELINE | 2 |
01-701-1015 | BMI | 2014-01-16 | 2 | NA | WEEK 2 | 3 |
01-701-1015 | BMI | 2014-01-30 | 4 | NA | WEEK 4 | 4 |
01-701-1015 | BMI | 2014-02-12 | 6 | NA | WEEK 6 | 5 |
01-701-1015 | BMI | 2014-03-05 | 8 | NA | WEEK 8 | 6 |
01-701-1015 | BMI | 2014-03-26 | 12 | NA | WEEK 12 | 7 |
01-701-1015 | BMI | 2014-05-07 | 16 | NA | WEEK 16 | 8 |
01-701-1015 | BMI | 2014-05-21 | 20 | NA | WEEK 20 | 9 |
01-701-1015 | BMI | 2014-06-18 | 24 | NA | WEEK 24 | 10 |
AVALCATx
)Admiral does not currently have a generic function to aid in
assigning AVALCATx
/ AVALCAxN
values. Below is
a simple example of how these values may be assigned:
avalcat_lookup <- tibble::tribble(
~PARAMCD, ~AVALCA1N, ~AVALCAT1,
"HEIGHT", 1, ">140 cm",
"HEIGHT", 2, "<= 140 cm"
)
format_avalcat1n <- function(param, aval) {
case_when(
param == "HEIGHT" & aval > 140 ~ 1,
param == "HEIGHT" & aval <= 140 ~ 2
)
}
advs <- advs %>%
mutate(AVALCA1N = format_avalcat1n(param = PARAMCD, aval = AVAL)) %>%
derive_vars_merged(
avalcat_lookup,
by = exprs(PARAMCD, AVALCA1N)
)
USUBJID | PARAMCD | AVAL | AVALCA1N | AVALCAT1 |
---|---|---|---|---|
01-701-1015 | HEIGHT | 147.32 | 1 | >140 cm |
01-701-1023 | HEIGHT | 162.56 | 1 | >140 cm |
01-703-1086 | HEIGHT | 195.58 | 1 | >140 cm |
01-703-1096 | HEIGHT | 160.02 | 1 | >140 cm |
01-707-1037 | HEIGHT | 152.40 | 1 | >140 cm |
01-716-1024 | HEIGHT | 154.94 | 1 | >140 cm |
If needed, the other ADSL
variables can now be added.
List of ADSL variables already merged held in vector
adsl_vars
advs <- advs %>%
derive_vars_merged(
dataset_add = select(adsl, !!!negate_vars(adsl_vars)),
by_vars = exprs(STUDYID, USUBJID)
)
USUBJID | RFSTDTC | RFENDTC | DTHDTC | DTHFL | AGE | AGEU |
---|---|---|---|---|---|---|
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
When deriving new rows for a data frame, it is essential the programmer takes time to insert this derivation in the correct location of the code. The location will vary depending on what previous computations should be retained on the new record and what computations must be done with the new records.
To add a new record based on the selection of a certain criterion
(e.g. minimum, maximum) derive_extreme_records()
can be
used. The new records include all variables of the selected records.
For each subject and Vital Signs parameter, add a record holding last
valid observation before end of treatment. Set AVISIT
to
"End of Treatment"
and assign a unique AVISITN
value.
advs_ex1 <- advs %>%
derive_extreme_records(
dataset_add = advs,
by_vars = exprs(STUDYID, USUBJID, PARAMCD),
order = exprs(ADT, AVISITN, ATPTN, AVAL),
mode = "last",
filter_add = (4 < AVISITN & AVISITN <= 12 & ANL01FL == "Y"),
set_values_to = exprs(
AVISIT = "End of Treatment",
AVISITN = 99,
DTYPE = "LOV"
)
)
USUBJID | PARAMCD | ADT | AVISITN | AVISIT | ATPTN | AVAL | DTYPE | ANL01FL |
---|---|---|---|---|---|---|---|---|
01-701-1015 | BMI | 2014-03-26 | 99 | End of Treatment | NA | 24.45264 | LOV | Y |
01-701-1015 | BMI | 2014-07-02 | 26 | Week 26 | NA | 24.65998 | NA | Y |
01-701-1015 | BMI | 2014-06-18 | 24 | Week 24 | NA | 24.45264 | NA | Y |
01-701-1015 | BMI | 2014-05-21 | 20 | Week 20 | NA | 24.45264 | NA | Y |
01-701-1015 | BMI | 2014-05-07 | 16 | Week 16 | NA | 24.45264 | NA | Y |
01-701-1015 | BMI | 2014-03-26 | 12 | Week 12 | NA | 24.45264 | NA | Y |
01-701-1015 | BMI | 2014-03-05 | 8 | Week 8 | NA | 24.45264 | NA | Y |
01-701-1015 | BMI | 2014-02-12 | 6 | Week 6 | NA | 24.45264 | NA | Y |
01-701-1015 | BMI | 2014-01-30 | 4 | Week 4 | NA | 24.87193 | NA | Y |
01-701-1015 | BMI | 2014-01-16 | 2 | Week 2 | NA | 24.45264 | NA | Y |
For each subject and Vital Signs parameter, add a record holding the
minimum value before end of treatment. If the minimum is attained by
multiple observations the first one is selected. Set AVISIT
to "Minimum on Treatment"
and assign a unique
AVISITN
value.
advs_ex1 <- advs %>%
derive_extreme_records(
dataset_add = advs,
by_vars = exprs(STUDYID, USUBJID, PARAMCD),
order = exprs(AVAL, ADT, AVISITN, ATPTN),
mode = "first",
filter_add = (4 < AVISITN & AVISITN <= 12 & ANL01FL == "Y" & !is.na(AVAL)),
set_values_to = exprs(
AVISIT = "Minimum on Treatment",
AVISITN = 98,
DTYPE = "MINIMUM"
)
)
USUBJID | PARAMCD | ADT | AVISITN | AVISIT | ATPTN | AVAL | DTYPE | ANL01FL |
---|---|---|---|---|---|---|---|---|
01-701-1015 | BMI | 2014-02-12 | 98 | Minimum on Treatment | NA | 24.45264 | MINIMUM | Y |
01-701-1015 | BMI | 2014-07-02 | 26 | Week 26 | NA | 24.65998 | NA | Y |
01-701-1015 | BMI | 2014-06-18 | 24 | Week 24 | NA | 24.45264 | NA | Y |
01-701-1015 | BMI | 2014-05-21 | 20 | Week 20 | NA | 24.45264 | NA | Y |
01-701-1015 | BMI | 2014-05-07 | 16 | Week 16 | NA | 24.45264 | NA | Y |
01-701-1015 | BMI | 2014-03-26 | 12 | Week 12 | NA | 24.45264 | NA | Y |
01-701-1015 | BMI | 2014-03-05 | 8 | Week 8 | NA | 24.45264 | NA | Y |
01-701-1015 | BMI | 2014-02-12 | 6 | Week 6 | NA | 24.45264 | NA | Y |
01-701-1015 | BMI | 2014-01-30 | 4 | Week 4 | NA | 24.87193 | NA | Y |
01-701-1015 | BMI | 2014-01-16 | 2 | Week 2 | NA | 24.45264 | NA | Y |
For adding new records based on aggregating records
derive_summary_records()
can be used. For the new records
only the variables specified by by_vars
and
set_values_to
are populated.
For each subject, Vital Signs parameter, visit, and date add a record
holding the average value for observations on that date. Set
DTYPE
to AVERAGE
.
advs_ex2 <- derive_summary_records(
advs,
dataset_add = advs,
by_vars = exprs(STUDYID, USUBJID, PARAMCD, VISITNUM, ADT),
set_values_to = exprs(
AVAL = mean(AVAL, na.rm = TRUE),
DTYPE = "AVERAGE"
)
)
USUBJID | PARAMCD | VISITNUM | ADT | AVAL | DTYPE |
---|---|---|---|---|---|
01-701-1015 | BMI | 1 | 2013-12-26 | 24.87193 | AVERAGE |
01-701-1015 | BMI | 1 | 2013-12-26 | 24.87193 | NA |
01-701-1015 | BMI | 3 | 2014-01-02 | 25.07927 | AVERAGE |
01-701-1015 | BMI | 3 | 2014-01-02 | 25.07927 | NA |
01-701-1015 | BMI | 4 | 2014-01-16 | 24.45264 | AVERAGE |
01-701-1015 | BMI | 4 | 2014-01-16 | 24.45264 | NA |
01-701-1015 | BMI | 5 | 2014-01-30 | 24.87193 | AVERAGE |
01-701-1015 | BMI | 5 | 2014-01-30 | 24.87193 | NA |
01-701-1015 | BMI | 7 | 2014-02-12 | 24.45264 | AVERAGE |
01-701-1015 | BMI | 7 | 2014-02-12 | 24.45264 | NA |
PARAMCD
)Use function derive_param_computed()
to create a new
PARAMCD
. Note that only variables specified in the
by_vars
argument will be populated in the newly created
records.
Below is an example of creating Mean Arterial Pressure
(PARAMCD = MAP2
) with an alternative formula.
advs_ex3 <- derive_param_computed(
advs,
by_vars = exprs(USUBJID, VISIT, ATPT),
parameters = c("SYSBP", "DIABP"),
set_values_to = exprs(
AVAL = (AVAL.SYSBP - AVAL.DIABP) / 3 + AVAL.DIABP,
PARAMCD = "MAP2",
PARAM = "Mean Arterial Pressure 2 (mmHg)"
)
)
USUBJID | PARAMCD | VISIT | ATPT | AVAL |
---|---|---|---|---|
01-701-1015 | DIABP | AMBUL ECG PLACEMENT | AFTER LYING DOWN FOR 5 MINUTES | 67.00000 |
01-701-1015 | MAP2 | AMBUL ECG PLACEMENT | AFTER LYING DOWN FOR 5 MINUTES | 90.33333 |
01-701-1015 | SYSBP | AMBUL ECG PLACEMENT | AFTER LYING DOWN FOR 5 MINUTES | 137.00000 |
01-701-1015 | DIABP | AMBUL ECG PLACEMENT | AFTER STANDING FOR 1 MINUTE | 61.00000 |
01-701-1015 | MAP2 | AMBUL ECG PLACEMENT | AFTER STANDING FOR 1 MINUTE | 83.00000 |
01-701-1015 | SYSBP | AMBUL ECG PLACEMENT | AFTER STANDING FOR 1 MINUTE | 127.00000 |
01-701-1015 | DIABP | AMBUL ECG PLACEMENT | AFTER STANDING FOR 3 MINUTES | 65.00000 |
01-701-1015 | MAP2 | AMBUL ECG PLACEMENT | AFTER STANDING FOR 3 MINUTES | 92.00000 |
01-701-1015 | SYSBP | AMBUL ECG PLACEMENT | AFTER STANDING FOR 3 MINUTES | 146.00000 |
01-701-1015 | DIABP | AMBUL ECG REMOVAL | AFTER LYING DOWN FOR 5 MINUTES | 72.00000 |
Adding labels and attributes for SAS transport files is supported by the following packages:
metacore: establish a common foundation for the use of metadata within an R session.
metatools: enable the use of metacore objects. Metatools can be used to build datasets or enhance columns in existing datasets as well as checking datasets against the metadata.
xportr: functionality to associate all metadata information to a local R data frame, perform data set level validation checks and convert into a transport v5 file(xpt).
NOTE: All these packages are in the experimental phase, but the vision is to have them associated with an End to End pipeline under the umbrella of the pharmaverse. An example of applying metadata and perform associated checks can be found at the pharmaverse E2E example.
ADaM | Sourcing Command |
---|---|
ADEG | use_ad_template("ADEG") |
ADVS | use_ad_template("ADVS") |
ADLB | use_ad_template("ADLB") |
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.