An example Clinical Events analysis dataset
+See also
+Other dataset:
+admiralvaccine_adface
,
+admiralvaccine_adis
,
+admiralvaccine_adsl
From 3f5dcb3b25aeaf9803afeb43237769d02deeff6c Mon Sep 17 00:00:00 2001
From: GitHub Actions
+
-adsl_vars <- exprs(RFSTDTC, RFENDTC)
+adsl_vars <- exprs(RFSTDTC, RFENDTC)
-adface <- derive_vars_merged(
+adface <- derive_vars_merged(
face,
dataset_add = adsl,
new_vars = adsl_vars,
- by_vars = exprs(STUDYID, USUBJID)
+ by_vars = exprs(STUDYID, USUBJID)
)
This call would return the input dataset with columns +
+This call would return the input dataset with columns
RFSTDTC
, RFENDTC
added.
VS
Domain
adface <- derive_fever_records(
dataset = adface,
- dataset_source = vs,
+ dataset_source = ungroup(vs),
filter_source = VSCAT == "REACTOGENICITY" & VSTESTCD == "TEMP",
faobj = "FEVER"
)
This call returns the input dataset with FEVER
records
+
This call returns the input dataset with FEVER
records
added if the input dataset does not have FEVER
records. If
the input dataset has FEVER
records, the output dataset
will be same as the input dataset.
ADTADTM
, ADTF
, ATMF
,
ADY
)
The function derive_vars_dt()
can be used to derive
+
The function derive_vars_dt()
can be used to derive
ADT
. This function allows the user to impute the date as
well.
Similarly, ADTM
can be created using the function
-derive_vars_dtm()
. Imputation can be done on both the date
+derive_vars_dtm()
. Imputation can be done on both the date
and time components of ADTM
.
Example calls:
-adface <- adface %>%
- derive_vars_dt(
+adface <- adface %>%
+ derive_vars_dt(
new_vars_prefix = "A",
dtc = FADTC
- ) %>%
- derive_vars_dtm(
+ ) %>%
+ derive_vars_dtm(
new_vars_prefix = "A",
dtc = FADTC,
highest_imputation = "n"
)
Once ADT
is derived, the function
-derive_vars_dy()
can be used to derive ADY
.
+derive_vars_dy()
can be used to derive ADY
.
This example assumes both ADT
and RFSTDTC
exist on the data frame.
-adface <- adface %>%
- mutate(RFSTDTC = as.Date(RFSTDTC)) %>%
- derive_vars_dy(reference_date = RFSTDTC, source_vars = exprs(ADT))
adface <- adface %>%
+ mutate(RFSTDTC = as.Date(RFSTDTC)) %>%
+ derive_vars_dy(reference_date = RFSTDTC, source_vars = exprs(ADT))
+
+
APxxSDT
,
@@ -307,29 +314,30 @@ APxxSDT
,
The admiral core package has separate functions to handle period variables since these variables are study specific.
-See the “Visit
+ See the “Visit
and Period Variables” vignette for more information. If the variables are not derived based on a period reference dataset,
they may be derived at a later point of the flow. For example, phases
like “Treatment Phase” and “Follow up” could be derived based on
treatment start and end date.
-
period_ref <- create_period_dataset(
+
period_ref <- create_period_dataset(
dataset = adsl,
- new_vars = exprs(
+ new_vars = exprs(
APERSDT = APxxSDT, APEREDT = APxxEDT, TRTA = TRTxxA,
TRTP = TRTxxP
)
)
-adface <- derive_vars_joined(
+adface <- derive_vars_joined(
adface,
dataset_add = period_ref,
- by_vars = exprs(STUDYID, USUBJID),
- filter_join = ADT >= APERSDT & ADT <= APEREDT
+ by_vars = exprs(STUDYID, USUBJID),
+ filter_join = ADT >= APERSDT & ADT <= APEREDT,
+ join_type = "all"
)
-adface <- adface %>%
+adface <- adface %>%
mutate(
AVALC = as.character(FASTRESC),
AVAL = suppressWarnings(as.numeric(FASTRESN)),
@@ -360,8 +368,8 @@ Derive Direct Mapping Variables= FATPT,
ATPTN = FATPTNUM
)
This call returns the input dataset with severity records derived +
+This call returns the input dataset with severity records derived from the diameter records for an event.
By default, we will populate SEV
and
Severity/Intensity
as the FATESTCD
and
@@ -396,48 +404,51 @@
derive_extreme_records()
.
-adface <- derive_extreme_records(
+adface <- derive_extreme_records(
dataset = adface,
+ dataset_add = adface,
filter_add = FATESTCD == "SEV",
- by_vars = exprs(USUBJID, FAOBJ, ATPTREF),
- order = exprs(AVAL),
+ by_vars = exprs(USUBJID, FAOBJ, ATPTREF),
+ order = exprs(AVAL),
check_type = "none",
mode = "last",
- set_values_to = exprs(
+ set_values_to = exprs(
FATEST = "Maximum Severity",
FATESTCD = "MAXSEV"
)
)
-adface <- derive_extreme_records(
+adface <- derive_extreme_records(
dataset = adface,
+ dataset_add = adface,
filter_add = FAOBJ %in% c("REDNESS", "SWELLING") & FATESTCD == "DIAMETER",
- by_vars = exprs(USUBJID, FAOBJ, FALNKGRP),
- order = exprs(AVAL),
+ by_vars = exprs(USUBJID, FAOBJ, FALNKGRP),
+ order = exprs(AVAL),
check_type = "none",
mode = "last",
- set_values_to = exprs(
+ set_values_to = exprs(
FATEST = "Maximum Diameter",
FATESTCD = "MAXDIAM"
)
)
-adface <- derive_extreme_records(
+adface <- derive_extreme_records(
dataset = adface,
+ dataset_add = adface,
filter_add = FAOBJ == "FEVER",
- by_vars = exprs(USUBJID, FAOBJ, ATPTREF),
- order = exprs(VSSTRESN),
+ by_vars = exprs(USUBJID, FAOBJ, ATPTREF),
+ order = exprs(VSSTRESN),
check_type = "none",
mode = "last",
- set_values_to = exprs(
+ set_values_to = exprs(
FATEST = "Maximum Temperature",
FATESTCD = "MAXTEMP"
)
)
This call returns the input dataset with maximum records added for +
+This call returns the input dataset with maximum records added for the severity, diameter, temperature.
PARAMCD
, PARAM
,
adface <- derive_vars_params(
dataset = adface,
lookup_dataset = lookup_dataset,
- merge_vars = exprs(PARAMCD, PARAMN)
+ merge_vars = exprs(PARAMCD, PARAMN)
)
PARAMCD
will be always derived from lookup dataset
+
PARAMCD
will be always derived from lookup dataset
whereas PARAMN
, PARAM
, PARCAT1
,
PARCAT2
can be either derived from lookup dataset if
mentioned in merge_vars
argument or derived in the
@@ -561,8 +572,8 @@
This call would return the input dataset with columns +
+This call would return the input dataset with columns
ANL01FL
, ANL02FL
added by default. This
function allows the user to change the name of the new variables
created.
adface <- derive_vars_event_flag(
dataset = adface,
- by_vars = exprs(USUBJID, FAOBJ, ATPTREF),
+ by_vars = exprs(USUBJID, FAOBJ, ATPTREF),
aval_cutoff = 2.5,
new_var1 = EVENTFL,
new_var2 = EVENTDFL
)
-
-This call would return the input dataset with columns +
+This call would return the input dataset with columns
EVENTFL
, EVENTDFL
added by default. This
function allows the user to change the name of the new variables created
as well.
-adsl <- adsl %>%
- convert_blanks_to_na() %>%
+adsl <- adsl %>%
+ convert_blanks_to_na() %>%
filter(!is.na(USUBJID))
-adface <- derive_vars_merged(
+adface <- derive_vars_merged(
dataset = adface,
- dataset_add = select(adsl, !!!negate_vars(adsl_vars)),
- by_vars = exprs(STUDYID, USUBJID)
+ dataset_add = select(adsl, !!!negate_vars(adsl_vars)),
+ by_vars = exprs(STUDYID, USUBJID)
)
Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson.
+Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson.
-library(dplyr)
+library(admiral)
+library(dplyr)
library(lubridate)
+library(admiraldev)
library(admiralvaccine)
+library(pharmaversesdtm)
library(metatools)
# Load source datasets
-data("vx_is")
-data("vx_suppis")
-data("vx_adsl")
+data("is_vaccine")
+data("suppis_vaccine")
+data("admiralvaccine_adsl")
# Convert blanks into NA
-is <- convert_blanks_to_na(vx_is)
-suppis <- convert_blanks_to_na(vx_suppis)
-adsl <- convert_blanks_to_na(vx_adsl)
-adis_avisit <- is_suppis %>%
+adis_avisit <- is_suppis %>%
mutate(
AVISITN = as.numeric(VISITNUM),
AVISIT = case_when(
@@ -191,7 +198,7 @@ Derive Timing Variables)
)
-adis_atpt <- adis_avisit %>%
+adis_atpt <- adis_avisit %>%
mutate(
ATPTN = as.numeric(VISITNUM / 10),
ATPT = case_when(
@@ -207,8 +214,8 @@ Derive Timing Variablesis.na(VISITNUM) ~ NA_character_
)
)
CUTOFF
values. Please,
adapt base on your objectives.
-
-
+
+
DTYPE
is filled only
when lab result is below Lower Limit of Quantitation.
-adis_or <- adis_parcat1_cutoff %>%
- filter(DERIVED == "ORIG") %>%
+adis_or <- adis_parcat1_cutoff %>%
+ filter(DERIVED == "ORIG") %>%
derive_var_aval_adis(
lower_rule = ISLLOQ / 2,
middle_rule = ISSTRESN,
@@ -390,8 +397,8 @@ Derive AVAL, AVALU and DTYPE Variables= 2
)
-adis_log_or <- adis_parcat1_cutoff %>%
- filter(DERIVED == "LOG10") %>%
+adis_log_or <- adis_parcat1_cutoff %>%
+ filter(DERIVED == "LOG10") %>%
derive_var_aval_adis(
lower_rule = log10(ISLLOQ / 2),
middle_rule = log10(ISSTRESN),
@@ -399,8 +406,8 @@ Derive AVAL, AVALU and DTYPE Variables= 2
)
-adis_4fold <- adis_parcat1_cutoff %>%
- filter(DERIVED == "4FOLD") %>%
+adis_4fold <- adis_parcat1_cutoff %>%
+ filter(DERIVED == "4FOLD") %>%
derive_var_aval_adis(
lower_rule = ISLLOQ,
middle_rule = ISSTRESN,
@@ -408,8 +415,8 @@ Derive AVAL, AVALU and DTYPE Variables= 2
)
-adis_log_4fold <- adis_parcat1_cutoff %>%
- filter(DERIVED == "LOG10 4FOLD") %>%
+adis_log_4fold <- adis_parcat1_cutoff %>%
+ filter(DERIVED == "LOG10 4FOLD") %>%
derive_var_aval_adis(
lower_rule = log10(ISLLOQ),
middle_rule = log10(ISSTRESN),
@@ -417,7 +424,7 @@ Derive AVAL, AVALU and DTYPE Variables= 2
)
-adis_aval_sercat1 <- bind_rows(adis_or, adis_log_or, adis_4fold, adis_log_4fold) %>%
+adis_aval_sercat1 <- bind_rows(adis_or, adis_log_or, adis_4fold, adis_log_4fold) %>%
mutate( # AVALU derivation (please delete if not needed for your study)
AVALU = ISSTRESU,
@@ -439,11 +446,11 @@ Derive AVAL, AVALU and DTYPE VariablesNA_character_, NA_real_
)
-adis_sercat1n <- derive_vars_merged_lookup(
+adis_sercat1n <- derive_vars_merged_lookup(
dataset = adis_aval_sercat1,
dataset_add = param_lookup2,
- new_vars = exprs(SERCAT1N),
- by_vars = exprs(SERCAT1)
+ new_vars = exprs(SERCAT1N),
+ by_vars = exprs(SERCAT1)
)
#> All `SERCAT1` are mapped.
@@ -452,7 +459,7 @@ Derive AVAL, AVALU and DTYPE Variables# Please update code when <,<=,>,>= are present in your lab results (in ISSTRESC)
if (any(names(adis_sercat1n) == "ISULOQ") == TRUE) {
- adis_dtype <- adis_sercat1n %>%
+ adis_dtype <- adis_sercat1n %>%
mutate(DTYPE = case_when(
DERIVED %in% c("ORIG", "LOG10") & !is.na(ISLLOQ) &
((ISSTRESN < ISLLOQ) | grepl("<", ISORRES)) ~ "HALFLLOQ",
@@ -463,15 +470,15 @@ Derive AVAL, AVALU and DTYPE Variables}
if (any(names(adis_sercat1n) == "ISULOQ") == FALSE) {
- adis_dtype <- adis_sercat1n %>%
+ adis_dtype <- adis_sercat1n %>%
mutate(DTYPE = case_when(
DERIVED %in% c("ORIG", "LOG10") & !is.na(ISLLOQ) &
((ISSTRESN < ISLLOQ) | grepl("<", ISORRES)) ~ "HALFLLOQ",
TRUE ~ NA_character_
))
}
# BASETYPE derivation
-adis_basetype <- derive_basetype_records(
+adis_basetype <- derive_basetype_records(
adis_dtype,
- basetypes = exprs("VISIT 1" = AVISITN %in% c(10, 30))
+ basetypes = exprs("VISIT 1" = AVISITN %in% c(10, 30))
)
# BASE derivation
-adis_base <- derive_var_base(
+adis_base <- derive_var_base(
adis_basetype,
- by_vars = exprs(STUDYID, USUBJID, PARAMN),
+ by_vars = exprs(STUDYID, USUBJID, PARAMN),
source_var = AVAL,
new_var = BASE,
filter = VISITNUM == 10
@@ -498,22 +505,22 @@ Derive BASE Variables# ABLFL derivation
-adis_ablfl <- restrict_derivation(
+adis_ablfl <- restrict_derivation(
adis_base,
derivation = derive_var_extreme_flag,
- args = params(
- by_vars = exprs(STUDYID, USUBJID, PARAMN),
- order = exprs(STUDYID, USUBJID, VISITNUM, PARAMN),
+ args = params(
+ by_vars = exprs(STUDYID, USUBJID, PARAMN),
+ order = exprs(STUDYID, USUBJID, VISITNUM, PARAMN),
new_var = ABLFL,
mode = "first"
),
filter = VISITNUM == 10 & !is.na(BASE)
-) %>%
+) %>%
arrange(STUDYID, USUBJID, !is.na(DERIVED), VISITNUM, PARAMN)
# BASECAT derivation
-adis_basecat <- adis_ablfl %>%
+adis_basecat <- adis_ablfl %>%
mutate(
BASECAT1 = case_when(
!grepl("L", PARAMCD) & BASE < 10 ~ "Titer value < 1:10",
@@ -522,8 +529,8 @@ Derive BASE Variablesgrepl("L", PARAMCD) & BASE >= 10 ~ "Titer value >= 1:10"
)
)
-adis_chg <- restrict_derivation(
+adis_chg <- restrict_derivation(
adis_basecat,
derivation = derive_var_chg,
filter = AVISITN > 10
)
-adis_r2b <- restrict_derivation(
+adis_r2b <- restrict_derivation(
adis_chg,
derivation = derive_var_analysis_ratio,
- args = params(
+ args = params(
numer_var = AVAL,
denom_var = BASE
),
filter = AVISITN > 10
-) %>%
- arrange(STUDYID, USUBJID, DERIVED, ISSEQ) %>%
+) %>%
+ arrange(STUDYID, USUBJID, DERIVED, ISSEQ) %>%
select(-DERIVED)
-period_ref <- create_period_dataset(
+period_ref <- create_period_dataset(
dataset = adsl,
- new_vars = exprs(APERSDT = APxxSDT, APEREDT = APxxEDT, TRTA = TRTxxA, TRTP = TRTxxP)
+ new_vars = exprs(APERSDT = APxxSDT, APEREDT = APxxEDT, TRTA = TRTxxA, TRTP = TRTxxP)
)
-adis_trt <- derive_vars_joined(
+adis_trt <- derive_vars_joined(
adis_crit,
dataset_add = period_ref,
- by_vars = exprs(STUDYID, USUBJID),
- filter_join = ADT >= APERSDT & ADT <= APEREDT
+ by_vars = exprs(STUDYID, USUBJID),
+ filter_join = ADT >= APERSDT & ADT <= APEREDT,
+ join_type = "all"
)
-adis_ppsrfl <- adis_trt %>%
+adis_ppsrfl <- adis_trt %>%
mutate(PPSRFL = if_else(VISITNUM %in% c(10, 30) & PPROTFL == "Y", "Y", NA_character_))
# Get list of ADSL variables not to be added to ADIS
-vx_adsl_vars <- exprs(RFSTDTC, PPROTFL)
+adsl_vars <- exprs(RFSTDTC, PPROTFL)
-adis <- derive_vars_merged(
+adis <- derive_vars_merged(
dataset = adis_ppsrfl,
- dataset_add = select(vx_adsl, !!!negate_vars(vx_adsl_vars)),
- by_vars = exprs(STUDYID, USUBJID)
+ dataset_add = select(admiralvaccine_adsl, !!!negate_vars(adsl_vars)),
+ by_vars = exprs(STUDYID, USUBJID)
)
Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson.
+Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson.
use_ad_template()
from {admiral}, e.g.,
+use_ad_template()
from {admiral}, e.g.,
-use_ad_template(
+use_ad_template(
adam_name = "adce",
save_path = "./ad_adce.R",
package = "admiralvaccine"
)
A list of all available templates can be obtained by
-list_all_templates()
from {admiral}:
list_all_templates()
from {admiral}:
-list_all_templates(package = "admiralvaccine")
+list_all_templates(package = "admiralvaccine")
#> Existing ADaM templates in package 'admiralvaccine':
#> • ADCE
#> • ADFACE
@@ -176,7 +180,7 @@ Support
-Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson.
+Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson.
diff --git a/main/articles/adsl.html b/main/articles/adsl.html
index d799b20b..9eb766a8 100644
--- a/main/articles/adsl.html
+++ b/main/articles/adsl.html
@@ -60,20 +60,24 @@
-
Changelog
- -
- Versions
-
+-
+
+
+
+
+
+-
+
+
+
+
+
-
@@ -149,21 +153,25 @@
Read in Data
library(admiral)
+library(admiralvaccine)
+library(pharmaversesdtm)
library(dplyr, warn.conflicts = FALSE)
-library(admiral.test)
library(lubridate)
library(stringr)
-data("vx_dm")
-data("vx_ex")
-dm <- convert_blanks_to_na(vx_dm)
-ex <- convert_blanks_to_na(vx_ex)
+library(admiraldev)
+
+data("dm_vaccine")
+data("ex_vaccine")
+
+dm <- convert_blanks_to_na(dm_vaccine)
+ex <- convert_blanks_to_na(ex_vaccine)
The DM
domain is used as the basis for
ADSL
:
The admiral core package has separate functions to handle period variables since these variables are study specific.
-See the “Visit
+ See the “Visit
and Period Variables” vignette for more information. If the variables are not derived based on a period reference dataset,
they may be derived at a later point of the flow. For example, phases
@@ -185,67 +193,67 @@ The mapping of the treatment variables is left to the ADaM
programmer. An example mapping for a study without periods may be:Derive Treatment Variables (
TRT0xP
,
-
adsl <- dm %>%
+
adsl <- dm %>%
mutate(
TRT01P = substring(ARM, 1, 9),
TRT02P = substring(ARM, 11, 100)
- ) %>%
- derive_vars_merged(
+ ) %>%
+ derive_vars_merged(
dataset_add = ex,
filter_add = EXLNKGRP == "VACCINATION 1",
- new_vars = exprs(TRT01A = EXTRT),
- by_vars = exprs(STUDYID, USUBJID)
- ) %>%
- derive_vars_merged(
+ new_vars = exprs(TRT01A = EXTRT),
+ by_vars = exprs(STUDYID, USUBJID)
+ ) %>%
+ derive_vars_merged(
dataset_add = ex,
filter_add = EXLNKGRP == "VACCINATION 2",
- new_vars = exprs(TRT02A = EXTRT),
- by_vars = exprs(STUDYID, USUBJID)
+ new_vars = exprs(TRT02A = EXTRT),
+ by_vars = exprs(STUDYID, USUBJID)
)
TRTSDTM
, TRTEDTM
, TRTDURD
)
The function derive_vars_merged()
can be used to derive
+
The function derive_vars_merged()
can be used to derive
the treatment start and end date/times using the ex
domain.
A pre-processing step for ex
is required to convert the
variable EXSTDTC
and EXSTDTC
to datetime
variables and impute missing date or time components. Conversion and
-imputation is done by derive_vars_dtm()
.
derive_vars_dtm()
.
Example calls:
# impute start and end time of exposure to first and last respectively, do not impute date
-ex_ext <- ex %>%
- derive_vars_dtm(
+ex_ext <- ex %>%
+ derive_vars_dtm(
dtc = EXSTDTC,
new_vars_prefix = "EXST"
- ) %>%
- derive_vars_dtm(
+ ) %>%
+ derive_vars_dtm(
dtc = EXENDTC,
new_vars_prefix = "EXEN"
)
-adsl <- adsl %>%
- derive_vars_merged(
+adsl <- adsl %>%
+ derive_vars_merged(
dataset_add = ex_ext,
filter_add = (EXDOSE > 0 |
(EXDOSE == 0 &
str_detect(EXTRT, "VACCINE"))) &
!is.na(EXSTDTM),
- new_vars = exprs(TRTSDTM = EXSTDTM),
- order = exprs(EXSTDTM, EXSEQ),
+ new_vars = exprs(TRTSDTM = EXSTDTM),
+ order = exprs(EXSTDTM, EXSEQ),
mode = "first",
- by_vars = exprs(STUDYID, USUBJID)
- ) %>%
- derive_vars_merged(
+ by_vars = exprs(STUDYID, USUBJID)
+ ) %>%
+ derive_vars_merged(
dataset_add = ex_ext,
filter_add = (EXDOSE > 0 |
(EXDOSE == 0 &
str_detect(EXTRT, "VACCINE"))) & !is.na(EXENDTM),
- new_vars = exprs(TRTEDTM = EXENDTM),
- order = exprs(EXENDTM, EXSEQ),
+ new_vars = exprs(TRTEDTM = EXENDTM),
+ order = exprs(EXENDTM, EXSEQ),
mode = "last",
- by_vars = exprs(STUDYID, USUBJID)
+ by_vars = exprs(STUDYID, USUBJID)
)
This call returns the original data frame with the column
TRTSDTM
, TRTSTMF
, TRTEDTM
, and
@@ -253,37 +261,37 @@
The datetime variables returned can be converted to dates using the
-derive_vars_dtm_to_dt()
function.
derive_vars_dtm_to_dt()
function.
-adsl <- adsl %>%
- derive_vars_dtm_to_dt(source_vars = exprs(TRTSDTM, TRTEDTM))
adsl <- adsl %>%
+ derive_vars_dtm_to_dt(source_vars = exprs(TRTSDTM, TRTEDTM))
Now, that TRTSDT
and TRTEDT
are derived,
-the function derive_var_trtdurd()
can be used to calculate
+the function derive_var_trtdurd()
can be used to calculate
the Treatment duration (TRTDURD
).
-adsl <- adsl %>%
- derive_var_trtdurd()
adsl <- adsl %>%
+ derive_var_trtdurd()
SAFFL
)
Since the populations flags are mainly company/study specific no
dedicated functions are provided, but in most cases they can easily be
-derived using derive_var_merged_exist_flag()
.
derive_var_merged_exist_flag()
.
An example of an implementation could be:
-adsl <- derive_var_merged_exist_flag(
+adsl <- derive_var_merged_exist_flag(
dataset = adsl,
dataset_add = ex,
- by_vars = exprs(STUDYID, USUBJID),
+ by_vars = exprs(STUDYID, USUBJID),
new_var = SAFFL,
condition = (EXDOSE > 0 | (EXDOSE == 0 & str_detect(EXTRT, "VACCINE")))
-) %>%
+) %>%
mutate(
PPROTFL = "Y"
)
This call would return the input dataset with columns +
+This call would return the input dataset with columns
VAX01DT
, VAX02DT
added.
-adsl <- adsl %>%
+adsl <- adsl %>%
mutate(
AP01SDT = VAX01DT,
AP01EDT = if_else(!is.na(VAX02DT), VAX02DT - 1, as.Date(RFPENDTC)),
AP02SDT = if_else(!is.na(VAX02DT), VAX02DT, NA_Date_),
AP02EDT = if_else(!is.na(AP02SDT), as.Date(RFPENDTC), NA_Date_)
)
This call would return the input dataset with columns +
+This call would return the input dataset with columns
AP01SDT
, AP01EDT
, AP02SDT
,
AP02EDT
added.
derive_vars_merged()
- Merge Variables from a Dataset
+derive_vars_merged()
- Merge Variables from a Dataset
to the Input Datasetderive_var_merged_exist_flag()
- Merge an Existence
+derive_var_merged_exist_flag()
- Merge an Existence
Flagderive_var_merged_summary()
- Merge a Summary
+derive_var_merged_summary()
- Merge a Summary
VariableSee also Generic
+ See also Generic
Functions. Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Vikram S. Author.
+ Arjun Rubalingam. Author.
Arjun Rubalingam. Author.
+ Vikram S. Author.
Jagadish Katam. Contributor.
Lee Armishaw. Contributor.
+ Andrea Pammolli. Contributor.
Source: Saha S, S V, Straub B, Rubalingam A, Kanagaraj D, Baratin F, Purna Bollu Y, Augustyns I, Bodicherla K, Delanghe H (2023).
+ Saha S, Rubalingam A, Straub B, S V, Kanagaraj D, Baratin F, Purna Bollu Y, Augustyns I, Bodicherla K, Delanghe H (2023).
admiralvaccine: Vaccine Extension Package for ADaM in 'R' Asset Library.
https://pharmaverse.github.io/admiralvaccine/,
https://github.com/pharmaverse/admiralvaccine/.
Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Thank you for your Pull Request! We have developed this task checklist from the Development Process Guide to help with the final steps of the process. Completing the below tasks helps to ensure our reviewers can maximize their time on your code as well as making sure the admiral codebase remains robust and consistent. Thank you for your Pull Request! We have developed this task checklist from the Development Process Guide to help with the final steps of the process. Completing the below tasks helps to ensure our reviewers can maximize their time on your code as well as making sure the admiral codebase remains robust and consistent. Please check off each taskbox as an acknowledgment that you completed the task or check off that it is not relevant to your Pull Request. This checklist is part of the Github Action workflows and the Pull Request will not be merged into the Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Maintainer: Sukalpo Saha sukalpo.saha@pfizer.com Authors: Vikram S vikram.s@pfizer.com Authors: Arjun Rubalingam arjun.rubalingam@pfizer.com Ben Straub ben.x.straub@gsk.com Arjun Rubalingam Vikram S Dhivya Kanagaraj Federico Baratin Yamini Purna Bollu Ankur Jindal [contributor] Jayashree V [contributor] Jagadish Katam [contributor] Lee Armishaw [contributor] Andrea Pammolli [contributor] Daniele Bottigliengo [contributor] Ranya Ben Hsain [contributor] Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. An example Clinical Events analysis dataset Other dataset:
+ Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Site built with pkgdown 2.0.7. An example Findings About Clinical Events analysis dataset An object of class Other dataset:
+ Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Site built with pkgdown 2.0.7. An example Immunogenicity Specimen Assessments analysis dataset An object of class Other dataset:
+ Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Site built with pkgdown 2.0.7. An example Subject Level analysis dataset Other dataset:
+ Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Site built with pkgdown 2.0.7. Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. You can run You can run Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson. Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson.Example Script
All vignettes
Authors
Authors
Authors
Authors
Citation
DESCRIPTION
@Manual{,
title = {admiralvaccine: Vaccine Extension Package for ADaM in 'R' Asset Library},
- author = {Sukalpo Saha and Vikram S and Ben Straub and Arjun Rubalingam and Dhivya Kanagaraj and Federico Baratin and Yamini {Purna Bollu} and Ilse Augustyns and Kalyani Bodicherla and Hilde Delanghe},
+ author = {Sukalpo Saha and Arjun Rubalingam and Ben Straub and Vikram S and Dhivya Kanagaraj and Federico Baratin and Yamini {Purna Bollu} and Ilse Augustyns and Kalyani Bodicherla and Hilde Delanghe},
year = {2023},
note = {https://pharmaverse.github.io/admiralvaccine/,
https://github.com/pharmaverse/admiralvaccine/},
@@ -198,7 +206,7 @@
Citation
Citation
Developers
Author, maintainer
Author
Author
Author
Author
Author
Author
Author
Author Dev status
Documentation
devel
branch until you have checked off each task.styler::style_file()
to style R and Rmd filesNEWS.md
if the changes pertain to a user-facing function (i.e. it has an @export
tag) or documentation aimed at users (rather than developers)
pkgdown::build_site()
and check that all affected examples are displayed correctly and that all new functions occur on the “Reference” page.pkgdown::build_site()
and check that all affected examples are displayed correctly and that all new functions occur on the “Reference” page.
lintr::lint_package()
@@ -116,7 +120,7 @@
See also
Author
Author<
Author<
See also
+ admiralvaccine_adface
,
+admiralvaccine_adis
,
+admiralvaccine_adsl
Findings About Clinical Events Analysis Dataset - Vaccine Specific
+ Source: R/data.R
+ admiralvaccine_adface.Rd
Format
+ tbl_df
(inherits from tbl
, data.frame
) with 371 rows and 60 columns.See also
+ admiralvaccine_adce
,
+admiralvaccine_adis
,
+admiralvaccine_adsl
Immunogenicity Specimen Assessments Analysis Dataset - Vaccine Specific
+ Source: R/data.R
+ admiralvaccine_adis.Rd
Format
+ tbl_df
(inherits from tbl
, data.frame
) with 64 rows and 102 columns.See also
+ admiralvaccine_adce
,
+admiralvaccine_adface
,
+admiralvaccine_adsl
See also
+ admiralvaccine_adce
,
+admiralvaccine_adface
,
+admiralvaccine_adis
Examples
-
Author<
Examples
library(tibble)
library(dplyr)
-library(admiraldev)
+library(admiraldev)
#>
#> Attaching package: ‘admiraldev’
#> The following objects are masked from ‘package:dplyr’:
@@ -181,7 +185,7 @@
Examples
-
Author<
Examples
library(tibble)
library(admiral)
-library(admiraldev)
+library(admiraldev)
library(dplyr)
library(rlang)
#>
@@ -214,7 +218,7 @@
Examples
-
Author<
Examples
library(tibble)
library(admiral)
-library(admiraldev)
+library(admiraldev)
library(dplyr)
input <- tribble(
@@ -208,7 +212,7 @@
Examples
-
Examples
derive_vars_event_flag(
dataset = input,
- by_vars = exprs(USUBJID, FAOBJ, ATPTREF),
+ by_vars = exprs(USUBJID, FAOBJ, ATPTREF),
aval_cutoff = 2.5,
new_var1 = EVENTL,
new_var2 = EVENTDL
@@ -214,7 +218,7 @@
Examples
-
Author<
Examples
library(dplyr)
-library(admiraldev)
+library(admiraldev)
library(admiral)
library(tibble)
@@ -207,7 +211,7 @@
Examples
-
Exampleslibrary(tibble)
library(admiral)
library(dplyr)
+library(pharmaversesdtm)
derive_vars_merged_vaccine(
- dataset = vx_face,
- dataset_ex = vx_ex,
+ dataset = face_vaccine,
+ dataset_ex = ex_vaccine,
dataset_supp = NULL,
dataset_suppex = NULL,
- by_vars_sys = exprs(USUBJID, FATPTREF = EXLNKGRP),
- by_vars_adms = exprs(USUBJID, FATPTREF = EXLNKGRP, FALOC = EXLOC, FALAT = EXLAT),
- ex_vars = exprs(EXTRT, EXDOSE, EXDOSU, EXSTDTC, EXENDTC)
+ by_vars_sys = exprs(USUBJID, FATPTREF = EXLNKGRP),
+ by_vars_adms = exprs(USUBJID, FATPTREF = EXLNKGRP, FALOC = EXLOC, FALAT = EXLAT),
+ ex_vars = exprs(EXTRT, EXDOSE, EXDOSU, EXSTDTC, EXENDTC)
) %>%
select(USUBJID, FATPTREF, FALOC, FALAT, EXTRT, EXDOSE, EXDOSU, EXSTDTC, EXENDTC) %>%
head(10)
@@ -193,13 +198,13 @@
Examples#> 10 ABC-1001 VACCINATION 1 DELTOID MUS… LEFT VACC… 1 SYRIN… 2021-1… 2021-1…
derive_vars_merged_vaccine(
- dataset = vx_face,
- dataset_ex = vx_ex,
- dataset_supp = vx_suppface,
- dataset_suppex = vx_suppex,
- by_vars_sys = exprs(USUBJID, FATPTREF = EXLNKGRP),
- by_vars_adms = exprs(USUBJID, FATPTREF = EXLNKGRP, FALOC = EXLOC, FALAT = EXLAT),
- ex_vars = exprs(EXTRT, EXDOSE, EXDOSU, EXSTDTC, EXENDTC)
+ dataset = face_vaccine,
+ dataset_ex = ex_vaccine,
+ dataset_supp = suppface_vaccine,
+ dataset_suppex = suppex_vaccine,
+ by_vars_sys = exprs(USUBJID, FATPTREF = EXLNKGRP),
+ by_vars_adms = exprs(USUBJID, FATPTREF = EXLNKGRP, FALOC = EXLOC, FALAT = EXLAT),
+ ex_vars = exprs(EXTRT, EXDOSE, EXDOSU, EXSTDTC, EXENDTC)
) %>%
filter(CLTYP == "DAIRY") %>%
select(USUBJID, FATPTREF, CLTYP, EXTRT, EXDOSE, EXDOSU, EXSTDTC, EXENDTC)
@@ -217,7 +222,7 @@
Examples
-
Examplesderive_vars_params(
dataset = input,
lookup_dataset = lookup_dataset,
- merge_vars = exprs(PARAMCD, PARAMN)
+ merge_vars = exprs(PARAMCD, PARAMN)
)
#> # A tibble: 9 × 13
#> USUBJID FACAT FASCAT FATESTCD PARAM FAOBJ FATEST FALOC FALAT PARAMCD PARAMN
@@ -196,7 +200,7 @@
Examples
-
Examplesderive_vars_vaxdt(
dataset = input,
dataset_adsl = adsl,
- by_vars = exprs(USUBJID, VISITNUM),
- order = exprs(USUBJID, VISITNUM, VISIT, EXSTDTC)
+ by_vars = exprs(USUBJID, VISITNUM),
+ order = exprs(USUBJID, VISITNUM, VISIT, EXSTDTC)
)
#> # A tibble: 2 × 6
#> USUBJID SEX AGE VAX01DT VAX02DT VAX03DT
@@ -170,7 +174,7 @@
Examples
-
ADFACE-specific
Example Datasets
- admiral::use_ad_template()
to produce additional datasetsadmiral::use_ad_template()
to produce additional datasetsExample Datasets
vx_adsl
- vx_ce
- vx_dm
- vx_ex
- vx_face
- vx_is
- vx_suppce
- vx_suppdm
- vx_suppex
+ admiralvaccine_adce
vx_suppface
+ admiralvaccine_adface
vx_suppis
+ admiralvaccine_adis
vx_vs
+ admiralvaccine_adsl
Examples
max_flag(
dataset = input,
- by_vars = exprs(USUBJID, FAOBJ, FATPTREF, PARAMCD),
+ by_vars = exprs(USUBJID, FAOBJ, FATPTREF, PARAMCD),
fl = "ANL01FL"
)
#> Joining with `by = join_by(USUBJID, FAOBJ, FATESTCD, FATPTREF, AVAL, FATPT,
@@ -145,7 +149,7 @@
Examples
-
Examples
-