Creating ADFACE"
In admiralvaccine: Vaccine Extension Package for ADaM in 'R' Asset Library

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Introduction

This article describes about creating ADFACE ADaM dataset which is part of Vaccine - Reactogenicity based on the Center for Biologics Evaluation and Research (CBER) guidelines.

See the below links for more information:

Center for Biologics Evaluation and Research (CBER) Guidelines

Therapeutic Area Data Standards User Guide for Vaccines (TAUG-Vax)

Examples are currently tested using ADSL (ADaM) and face, vs, ex (SDTM) inputs.

Note: All examples assume CDISC SDTM and/or ADaM format as input unless otherwise specified.

Programming Workflow

Read in Data
Pre-processing of Input Dataset
Merge FACE with EX
Merge Required ADSL Variables Needed for Analysis
Derive Fever Records from VS Domain
Derive/Impute Numeric Date/Time and Analysis Day (ADT, ADTM, ADY, ADTF, ATMF)
Derive Period Variables (e.g. APxxSDT, APxxEDT, ...)
Derive Direct Mapping Variables
Derive Severity Records for Administration Site Events
Derive Maximum Records
Assign PARAMCD, PARAM, PARAMN, PARCAT1
Derive Maximum Severity Flag
Derive Event Occurrence Flag
Post-processing of the Dataset
Add ADSL Variables

Read in Data {#readdata}

To start, all data frames needed for the creation of ADFACE should be read into the environment. Some of the data frames needed are VS,EX and FACE.

library(admiral)
library(admiralvaccine)
library(admiraldev)
library(pharmaversesdtm)
library(dplyr, warn.conflicts = FALSE)
library(lubridate)
library(stringr)
library(tidyr)
library(tibble)

data("face_vaccine")
data("suppface_vaccine")
data("ex_vaccine")
data("suppex_vaccine")
data("vs_vaccine")
data("admiralvaccine_adsl")

face <- convert_blanks_to_na(face_vaccine)
ex <- convert_blanks_to_na(ex_vaccine)
vs <- convert_blanks_to_na(vs_vaccine)
suppface <- convert_blanks_to_na(suppface_vaccine)
suppex <- convert_blanks_to_na(suppex_vaccine)
adsl <- convert_blanks_to_na(admiralvaccine_adsl)

Pre-processing of Input Dataset {#input}

This step involves company-specific pre-processing of required input dataset for further analysis. In this step, we will filter records that has only reactogenicity events and combine the face and ex with their supplementary datasets suppface and suppex respectively.

face <- face %>%
  filter(FACAT == "REACTOGENICITY" & grepl("ADMIN|SYS", FASCAT)) %>%
  mutate(FAOBJ = str_to_upper(FAOBJ)) %>%
  metatools::combine_supp(suppface)
ex <- metatools::combine_supp(ex, suppex)

dataset_vignette(
  face,
  display_vars = exprs(USUBJID, FAOBJ, FATESTCD, FACAT, FASCAT, FATPTREF, FADTC)
)

Merge `FACE` with `EX` {#merge}

In this step, we will merge face with ex domain and add required variables from ex domain to the input dataset. If subjects have multiple vaccination at same visit then this function will not merge input dataset with ex dataset and throws a warning.

The function derive_vars_merged_vaccine() is used to merge face with ex domain.

adface <- derive_vars_merged_vaccine(
  dataset = face,
  dataset_ex = ex,
  by_vars_sys = exprs(USUBJID, FATPTREF = EXLNKGRP),
  by_vars_adms = exprs(USUBJID, FATPTREF = EXLNKGRP, FALOC = EXLOC, FALAT = EXLAT),
  ex_vars = exprs(EXTRT, EXDOSE, EXSEQ, EXSTDTC, EXENDTC, VISIT, VISITNUM)
)

dataset_vignette(
  adface,
  display_vars = exprs(USUBJID, FAOBJ, FATESTCD, FATPTREF, EXTRT)
)

This call would return the input dataset with columns from ex_vars added if the subjects does not have multiple vaccination at same visit.

Though the function will throw warning if subjects have multiple vaccination at same visit, this call would return the input dataset merging it with supplementary dataset.

Merge Required `ADSL` Variables Needed for Analysis {#adsl}

At this step, it may be useful to join ADSL to your face 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(RFSTDTC, RFENDTC)

adface <- derive_vars_merged(
  face,
  dataset_add = adsl,
  new_vars = adsl_vars,
  by_vars = get_admiral_option("subject_keys")
)

dataset_vignette(
  adface,
  display_vars = exprs(USUBJID, RFSTDTC, RFENDTC)
)

This call would return the input dataset with columns RFSTDTC, RFENDTC added.

Derive Fever Records from `VS` Domain {#fever}

In this step, we will merge fever records from the VS domain with the input dataset if the fever records does not present in the input dataset.

The function derive_fever_records() is used to merge fever records. These records will also be used in maximum temperature calculation.

adface <- derive_fever_records(
  dataset = adface,
  dataset_source = ungroup(vs),
  filter_source = VSCAT == "REACTOGENICITY" & VSTESTCD == "TEMP",
  faobj = "FEVER"
)

dataset_vignette(
  adface,
  display_vars = exprs(USUBJID, FAOBJ, FATESTCD, FAORRES, VSSTRESN),
  filter = FAOBJ == "FEVER"
)

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.

Derive/Impute Numeric Date/Time and Analysis Day (`ADT`, `ADTM`, `ADTF`, `ATMF`, `ADY`) {#datetime}

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 and time components of ADTM.

Example calls:

adface <- adface %>%
  derive_vars_dt(
    new_vars_prefix = "A",
    dtc = FADTC
  ) %>%
  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. 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))

dataset_vignette(
  adface,
  display_vars = exprs(USUBJID, FATPTREF, FAOBJ, ADT, ADTM, ADY)
)

Derive Period Variables (e.g. `APxxSDT`, `APxxEDT`, ...) {#periodvars}

The {admiral} core package has separate functions to handle period variables since these variables are study specific.

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(
  dataset = adsl,
  new_vars = exprs(
    APERSDT = APxxSDT, APEREDT = APxxEDT, TRTA = TRTxxA,
    TRTP = TRTxxP
  )
)

adface <- derive_vars_joined(
  adface,
  dataset_add = period_ref,
  by_vars = get_admiral_option("subject_keys"),
  filter_join = ADT >= APERSDT & ADT <= APEREDT,
  join_type = "all"
)

dataset_vignette(
  adface,
  display_vars = exprs(USUBJID, APERSDT, APEREDT, TRTA, TRTP)
)

Derive Direct Mapping Variables {#mapping}

In this step,we will create the user defined function to assign AVAL values from AVALC which will be used in further steps.

The user defined functions would look like the following:

sev_to_numeric <- function(x, y) {
  case_when(
    x == "NONE" ~ 0,
    x == "MILD" ~ 1,
    x == "MODERATE" ~ 2,
    x == "SEVERE" ~ 3,
    TRUE ~ y
  )
}

The mapping of these variables is left to the User. An example mapping may be:

adface <- adface %>%
  mutate(
    AVALC = as.character(FASTRESC),
    AVAL = suppressWarnings(as.numeric(FASTRESN)),
    AVAL = sev_to_numeric(AVALC, AVAL),
    ATPTREF = FATPTREF,
    ATPT = FATPT,
    ATPTN = FATPTNUM
  )

dataset_vignette(
  adface,
  display_vars = exprs(USUBJID, FAOBJ, AVAL, AVALC, ATPTREF, ATPTN)
)

Derive AN01FL

Creating ANL01FL which would flag the records that will be considered for analysis and if there is any Investigator and Subject record for the same day, it would flag the Investigator record over the subject record.

Note: Please, consider which assessment is needed for your analysis. If you want to prioritize Investigator assessment, please proceed as follows. Otherwise, change FAEVAL order.

adface <- adface %>% derive_var_extreme_flag(
  by = exprs(STUDYID, USUBJID, FATPTREF, FAOBJ, FATESTCD, FATPTNUM),
  order = exprs(STUDYID, USUBJID, FATPTREF, FAOBJ, FATESTCD, FATPTNUM, FAEVAL),
  new_var = ANL01FL,
  mode = "first",
  true_value = "Y",
  false_value = NA_character_
)

dataset_vignette(
  adface,
  display_vars = exprs(USUBJID, FAOBJ, ATPTREF, FATESTCD, FATEST, AVAL, ANL01FL),
  filter = ANL01FL == "Y"
)

Derive Severity Records for Administration Site Events {#sev}

The function derive_diam_to_sev_records() is used to derive the severity records from the diameter records for an event.

The severity records created will be useful for calculating the maximum severity.

adface <- derive_diam_to_sev_records(
  dataset = adface,
  filter_add = ANL01FL == "Y",
  diam_code = "DIAMETER",
  faobj_values = c("REDNESS", "SWELLING"),
  testcd_sev = "SEV",
  test_sev = "Severity/Intensity",
  none = 0,
  mild = 2,
  mod = 5,
  sev = 10
)

dataset_vignette(
  adface,
  display_vars = exprs(USUBJID, FAOBJ, ATPTREF, FATESTCD, FATEST, AVAL, AVALC),
  filter = FATESTCD == "SEV"
)

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 FATEST for the newly added records. The function allows the user to change the FATESTCD and FATEST as well.

Derive Maximum Records{#max}

In this step, we will derive maximum records for severity, diameter, temperature using the function derive_extreme_records().

adface <- derive_extreme_records(
  dataset = adface,
  dataset_add = adface,
  filter_add = FATESTCD == "SEV" & ANL01FL == "Y",
  by_vars = exprs(USUBJID, FAOBJ, ATPTREF),
  order = exprs(AVAL),
  check_type = "none",
  mode = "last",
  set_values_to = exprs(
    FATEST = "Maximum Severity",
    FATESTCD = "MAXSEV"
  )
)

adface <- derive_extreme_records(
  dataset = adface,
  dataset_add = adface,
  filter_add = FAOBJ %in% c("REDNESS", "SWELLING") & FATESTCD == "DIAMETER" & ANL01FL == "Y",
  by_vars = exprs(USUBJID, FAOBJ, FALNKGRP),
  order = exprs(AVAL),
  check_type = "none",
  mode = "last",
  set_values_to = exprs(
    FATEST = "Maximum Diameter",
    FATESTCD = "MAXDIAM"
  )
)

adface <- derive_extreme_records(
  dataset = adface,
  dataset_add = adface,
  filter_add = FAOBJ == "FEVER" & ANL01FL == "Y",
  by_vars = exprs(USUBJID, FAOBJ, ATPTREF),
  order = exprs(VSSTRESN),
  check_type = "none",
  mode = "last",
  set_values_to = exprs(
    FATEST = "Maximum Temperature",
    FATESTCD = "MAXTEMP"
  )
)

dataset_vignette(
  adface,
  display_vars = exprs(USUBJID, FAOBJ, ATPTREF, FATESTCD, FATEST, AVAL, AVALC),
  filter = FATESTCD %in% c("MAXSEV", "MAXDIAM", "MAXTEMP")
)

This call returns the input dataset with maximum records added for the severity, diameter, temperature.

Assign `PARAMCD`, `PARAM`, `PARAMN`, `PARCAT1` {#paramcd}

To assign parameter level values such as PARAMCD, PARAM, PARAMN, etc., a lookup needs to be created to join to the source data. PARCAT1, PARCAT2 variables are assigned from FACAT, FASCAT variables.

For example, when creating ADFACE dataset, a lookup based on the SDTM --TESTCD value can be created:

FATESTCD | PARAMCD | PARAMN |FATEST | FAOBJ --------- | --------- | -------- | ------- SEV | SEVREDN | 1 | Severity | Redness DIAMETER | DIARE | 2 | Diameter | Redness MAXDIAM | MDIRE | 3 | Maximum Diameter cm | Redness MAXTEMP | MAXTEMP | 4 | Maximum Temperature | Fever MAXSEV | MAXSWEL | 5 | Maximum Severity | Swelling OCCUR | OCFEVER | 6 | Occurrence Indicator | Fever OCCUR | OCERYTH | 7 | Occurrence Indicator | Erythema SEV | SEVPAIN | 8 | Severity | Pain at Injection site OCCUR | OCPAIN | 9 | Occurrence Indicator | Pain at Injection site OCCUR | OCSWEL | 10 | Occurrence Indicator | Swelling

This lookup can now be joined to the source data:

lookup_dataset <- tribble(
  ~FATESTCD, ~PARAMCD, ~PARAMN, ~FATEST, ~FAOBJ,
  "SEV", "SEVREDN", 1, "Severity/Intensity", "REDNESS",
  "DIAMETER", "DIARE", 2, "Diameter", "REDNESS",
  "MAXDIAM", "MDIRE", 3, "Maximum Diameter", "REDNESS",
  "MAXTEMP", "MAXTEMP", 4, "Maximum Temperature", "FEVER",
  "OCCUR", "OCFEVER", 5, "Occurrence Indicator", "FEVER",
  "OCCUR", "OCERYTH", 6, "Occurrence Indicator", "ERYTHEMA",
  "MAXSEV", "MAXSWEL", 7, "Maximum Severity", "SWELLING",
  "MAXSEV", "MAXREDN", 8, "Maximum Severity", "REDNESS",
  "MAXSEV", "MAXSFAT", 9, "Maximum Severity", "FATIGUE",
  "MAXSEV", "MAXSHEA", 10, "Maximum Severity", "HEADACHE",
  "MAXSEV", "MSEVNWJP", 11, "Maximum Severity", "NEW OR WORSENED JOINT PAIN",
  "MAXSEV", "MSEVNWMP", 12, "Maximum Severity", "NEW OR WORSENED MUSCLE PAIN",
  "OCCUR", "OCISR", 13, "Occurrence Indicator", "REDNESS",
  "OCCUR", "OCINS", 14, "Occurrence Indicator", "SWELLING",
  "OCCUR", "OCPIS", 15, "Occurrence Indicator", "PAIN AT INJECTION SITE",
  "OCCUR", "OCFATIG", 16, "Occurrence Indicator", "FATIGUE",
  "OCCUR", "OCHEAD", 17, "Occurrence Indicator", "HEADACHE",
  "OCCUR", "OCCHILLS", 18, "Occurrence Indicator", "CHILLS",
  "OCCUR", "OCDIAR", 19, "Occurrence Indicator", "DIARRHEA",
  "OCCUR", "OCCNWJP", 20, "Occurrence Indicator", "NEW OR WORSENED JOINT PAIN",
  "OCCUR", "OCCNWMP", 21, "Occurrence Indicator", "NEW OR WORSENED MUSCLE PAIN",
  "SEV", "SEVSWEL", 22, "Severity/Intensity", "SWELLING",
  "SEV", "SEVPIS", 23, "Severity/Intensity", "PAIN AT INJECTION SITE",
  "SEV", "SEVFAT", 24, "Severity/Intensity", "FATIGUE",
  "SEV", "SEVHEAD", 25, "Severity/Intensity", "HEADACHE",
  "SEV", "SEVDIAR", 26, "Severity/Intensity", "DIARRHEA",
  "SEV", "SEVNWJP", 27, "Severity/Intensity", "NEW OR WORSENED JOINT PAIN",
  "SEV", "SEVNWMP", 28, "Severity/Intensity", "NEW OR WORSENED MUSCLE PAIN",
  "MAXDIAM", "MDISW", 29, "Maximum Diameter", "SWELLING",
  "MAXSEV", "MAXSPIS", 30, "Maximum Severity", "PAIN AT INJECTION SITE",
  "OCCUR", "OCCVOM", 31, "Occurrence Indicator", "VOMITING",
  "DIAMETER", "DIASWEL", 32, "Diameter", "SWELLING"
)

adface <- derive_vars_params(
  dataset = adface,
  lookup_dataset = lookup_dataset,
  merge_vars = exprs(PARAMCD, PARAMN)
)

dataset_vignette(
  adface,
  display_vars = exprs(USUBJID, FAOBJ, ATPTREF, FATEST, PARAMCD, PARAM, PARAMN, PARCAT1, PARCAT2)
)

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 function.

Derive Maximum Severity Flag {#maxflag}

The function derive_vars_max_flag() is used to derive flag variable for the maximum values of an event.

flag1 - Flags the maximum value per subject per event per Vaccination. flag2 - Flags the maximum value per subject per event for Overall.

adface <- derive_vars_max_flag(
  dataset = adface,
  flag1 = "ANL02FL",
  flag2 = "ANL03FL"
)

dataset_vignette(
  adface,
  display_vars = exprs(USUBJID, FAOBJ, ATPTREF, AVAL, AVALC, ANL01FL, ANL02FL)
)

This call would return the input dataset with columns ANL02FL, ANL03FL added by default. This function allows the user to change the name of the new variables created.

Derive Event Occurrence Flag {#eventflag}

The function derive_vars_event_flag() is used to derive flag variable for the events that occurred.

new_var1 - Flags the record if at least one of the event occurred within the observation period. new_var2 - Flags the record if the event is occurred.

adface <- derive_vars_event_flag(
  dataset = adface,
  by_vars = exprs(USUBJID, FAOBJ, ATPTREF),
  aval_cutoff = 2.5,
  new_var1 = EVENTFL,
  new_var2 = EVENTDFL
)

dataset_vignette(
  adface,
  display_vars = exprs(USUBJID, FAOBJ, ATPTREF, AVAL, AVALC, EVENTFL, EVENTDFL)
)

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.

Post-processing of the Dataset {#post}

In this step, we will remove values for all the derived records in SDTM variables.

adface <- post_process_reacto(
  dataset = adface,
  filter_dataset = FATESTCD %in% c("MAXDIAM", "MAXSEV", "MAXTEMP") |
    (FATESTCD %in% c("OCCUR", "SEV") & FAOBJ %in% c("FEVER", "REDNESS", "SWELLING"))
)

Add ADSL variables {#adsl_vars}

If needed, the other ADSL variables can now be added. List of ADSL variables already merged held in vector adsl_vars

adsl <- adsl %>%
  convert_blanks_to_na() %>%
  filter(!is.na(USUBJID))

adface <- derive_vars_merged(
  dataset = adface,
  dataset_add = select(adsl, !!!negate_vars(adsl_vars)),
  by_vars = get_admiral_option("subject_keys")
)

dataset_vignette(
  adface,
  display_vars = exprs(USUBJID, TRTSDT, TRTEDT, AGE, SEX)
)

Example Script

ADaM | Sample Code ---- | -------------- ADFACE | ad_adface.R{target="_blank"}

Any scripts or data that you put into this service are public.

admiralvaccine documentation built on Sept. 11, 2024, 6:35 p.m.

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Note that we can't provide technical support on individual packages. You should contact the package authors for that.

admiralvaccine
Vaccine Extension Package for ADaM in 'R' Asset Library

Creating ADFACE"
In admiralvaccine: Vaccine Extension Package for ADaM in 'R' Asset Library

Introduction

Programming Workflow

Read in Data {#readdata}

Pre-processing of Input Dataset {#input}

Merge `FACE` with `EX` {#merge}

Merge Required `ADSL` Variables Needed for Analysis {#adsl}

Derive Fever Records from `VS` Domain {#fever}

Derive/Impute Numeric Date/Time and Analysis Day (`ADT`, `ADTM`, `ADTF`, `ATMF`, `ADY`) {#datetime}

Derive Period Variables (e.g. `APxxSDT`, `APxxEDT`, ...) {#periodvars}

Derive Direct Mapping Variables {#mapping}

Derive AN01FL

Derive Severity Records for Administration Site Events {#sev}

Derive Maximum Records{#max}

Assign `PARAMCD`, `PARAM`, `PARAMN`, `PARCAT1` {#paramcd}

Derive Maximum Severity Flag {#maxflag}

Derive Event Occurrence Flag {#eventflag}

Post-processing of the Dataset {#post}

Add ADSL variables {#adsl_vars}

Example Script

Try the admiralvaccine package in your browser

R Package Documentation

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admiralvaccine Vaccine Extension Package for ADaM in 'R' Asset Library

Creating ADFACE" In admiralvaccine: Vaccine Extension Package for ADaM in 'R' Asset Library

Introduction

Programming Workflow

Read in Data {#readdata}

Pre-processing of Input Dataset {#input}

Merge FACE with EX {#merge}

Merge Required ADSL Variables Needed for Analysis {#adsl}

Derive Fever Records from VS Domain {#fever}

Derive/Impute Numeric Date/Time and Analysis Day (ADT, ADTM, ADTF, ATMF, ADY) {#datetime}

Derive Period Variables (e.g. APxxSDT, APxxEDT, ...) {#periodvars}

Derive Direct Mapping Variables {#mapping}

Derive AN01FL

Derive Severity Records for Administration Site Events {#sev}

Derive Maximum Records{#max}

Assign PARAMCD, PARAM, PARAMN, PARCAT1 {#paramcd}

Derive Maximum Severity Flag {#maxflag}

Derive Event Occurrence Flag {#eventflag}

Post-processing of the Dataset {#post}

Add ADSL variables {#adsl_vars}

Example Script

Try the admiralvaccine package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

admiralvaccine
Vaccine Extension Package for ADaM in 'R' Asset Library

Creating ADFACE"
In admiralvaccine: Vaccine Extension Package for ADaM in 'R' Asset Library

Merge `FACE` with `EX` {#merge}

Merge Required `ADSL` Variables Needed for Analysis {#adsl}

Derive Fever Records from `VS` Domain {#fever}

Derive/Impute Numeric Date/Time and Analysis Day (`ADT`, `ADTM`, `ADTF`, `ATMF`, `ADY`) {#datetime}

Derive Period Variables (e.g. `APxxSDT`, `APxxEDT`, ...) {#periodvars}

Assign `PARAMCD`, `PARAM`, `PARAMN`, `PARCAT1` {#paramcd}