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

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(admiral)
link <- function(text, url) {
  return(
    paste0(
      "[", text, "]",
      "(", url, ")"
    )
  )
}
dyn_link <- function(text,
                     base_url,
                     relative_url = "",
                     # Change to TRUE when admiral adopts multiversion docs
                     is_multiversion = FALSE,
                     multiversion_default_ref = "main") {
  url <- paste(base_url, relative_url, sep = "/")
  if (is_multiversion) {
    url <- paste(
      base_url,
      Sys.getenv("BRANCH_NAME", multiversion_default_ref),
      relative_url,
      sep = "/"
    )
  }
  return(link(text, url))
}
# Other variables
admiral_homepage <- "https://pharmaverse.github.io/admiral"

Introduction

This article describes how to create an ADIS ADaM domain. The parameters derived reflects common vaccine immunogenicity endpoints.

Examples are currently presented and tested using ADSL (ADaM) and IS and SUPPIS (SDTM) inputs.

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

Programming Workflow

Read in Data
Combine IS with SUPPIS
Derive Timing Variables
Derive ADT and ADY variables
Parameters Derivation
Derive PARCAT1 and CUTOFFx Variables
Derive AVAL, AVALU and DTYPE Variables
Derive BASE Variables
Derive CHG and R2BASE Variables
Derive CRITx Variables
Derive TRTP/A Variables
Derive PPS Record Level Flag Variable
Add ADSL Variables

Read in Data {#readdata}

In this first step you may read all the input data you need in order to proceed with ADIS development. In this template, SDTM.IS, SDTM.SUPPIS and ADAM.ADSL has been used.

library(admiral)
library(dplyr)
library(lubridate)
library(admiraldev)
library(admiralvaccine)
library(pharmaversesdtm)
library(metatools)
library(pharmaversesdtm)

# Load source datasets
data("is_vaccine")
data("suppis_vaccine")
data("admiralvaccine_adsl")

# Convert blanks into NA
is <- convert_blanks_to_na(is_vaccine)
suppis <- convert_blanks_to_na(suppis_vaccine)
adsl <- convert_blanks_to_na(admiralvaccine_adsl)

Combine IS with SUPPIS {#combine_supp}

Combine IS with its supplemental domain SUPPIS.

is_suppis <- metatools::combine_supp(is, suppis)

Derive Timing Variables {#avisit}

Derive AVISIT, AVISITN, ATPT, ATPTN and ATPTREF variables. Please, update visit records according to your Study Design/Protocol. For the visit values, please refers to your ADAM SPECIFICATIONS.

adis <- is_suppis %>%
  mutate(
    AVISITN = as.numeric(VISITNUM),
    AVISIT = case_when(
      VISITNUM == 10 ~ "Visit 1",
      VISITNUM == 20 ~ "Visit 2",
      VISITNUM == 30 ~ "Visit 3",
      VISITNUM == 40 ~ "Visit 4",
      is.na(VISITNUM) ~ NA_character_
    ),
    ATPTN = as.numeric(VISITNUM / 10),
    ATPT = case_when(
      VISITNUM == 10 ~ "Visit 1 (Day 1)",
      VISITNUM == 20 ~ "Visit 2 (Day 31)",
      VISITNUM == 30 ~ "Visit 3 (Day 61)",
      VISITNUM == 40 ~ "Visit 4 (Day 121)",
      is.na(VISITNUM) ~ NA_character_
    ),
    ATPTREF = case_when(
      VISITNUM %in% c(10, 20) ~ "FIRST TREATMENT",
      VISITNUM %in% c(30, 40) ~ "SECOND TREATMENT",
      is.na(VISITNUM) ~ NA_character_
    )
  )

dataset_vignette(
  adis,
  display_vars = exprs(USUBJID, VISITNUM, ISTEST, ISORRES, AVISIT, AVISITN, ATPT, ATPTN, ATPTREF)
)

Derive ADT and ADY Variables {#adt}

For ADT derivation, please follow your imputation rules. In the example below:

when day is missing then 15 is imputed;
When both day and month are missing then 30-06 is imputed;
If input date is missing then no imputation is done;

For ADY derivation RFSTDTC has been used in this template.

If your derivation is different, please adapt.

# ADT derivation
# Add also PPROTFL from ADSL (to avoid additional merges) in order to derive
# PPSRFL at step 11.
adis <- derive_vars_dt(
  dataset = adis,
  new_vars_prefix = "A",
  dtc = ISDTC,
  highest_imputation = "M",
  date_imputation = "mid",
  flag_imputation = "none"
) %>%
  derive_vars_merged(
    dataset_add = adsl,
    new_vars = exprs(RFSTDTC, PPROTFL),
    by_vars = get_admiral_option("subject_keys")
  ) %>%
  mutate(
    ADT = as.Date(ADT),
    RFSTDTC = as.Date(RFSTDTC)
  ) %>%
  # ADY derivation
  derive_vars_dy(
    reference_date = RFSTDTC,
    source_vars = exprs(ADT)
  )

dataset_vignette(
  adis,
  display_vars = exprs(USUBJID, VISITNUM, ISTEST, ISORRES, ISDTC, RFSTDTC, ADT, ADY, PPROTFL)
)

Parameters Derivation {#param}

In this template, duplicated records for PARAMCD have been created. In particular, you may find 4 different parameters values:

Original values and relative log10 values;
4fold values and relative log10 values;

Please, add or remove datasets according to your study needs.

# Create record duplication in order to plot both original and LOG10 parameter values.
# Add also records related to 4fold.
# Please, keep or modify PARAM values according to your purposes.

is_log <- adis %>%
  mutate(
    DERIVED = "LOG10",
    ISSEQ = NA_real_
  )

is_4fold <- adis %>%
  mutate(
    DERIVED = "4FOLD",
    ISSEQ = NA_real_
  )

is_log_4fold <- adis %>%
  mutate(
    DERIVED = "LOG10 4FOLD",
    ISSEQ = NA_real_
  )

adis <- bind_rows(adis, is_log, is_4fold, is_log_4fold) %>%
  arrange(STUDYID, USUBJID, !is.na(DERIVED), ISSEQ) %>%
  mutate(DERIVED = if_else(is.na(DERIVED), "ORIG", DERIVED))


adis <- adis %>%
  mutate(
    # PARAMCD: for log values, concatenation of L and ISTESTCD.
    PARAMCD = case_when(
      DERIVED == "ORIG" ~ ISTESTCD,
      DERIVED == "LOG10" ~ paste0(ISTESTCD, "L"),
      DERIVED == "4FOLD" ~ paste0(ISTESTCD, "F"),
      # As per CDISC rule, PARAMCD should be 8 characters long. Please, adapt if needed
      DERIVED == "LOG10 4FOLD" ~ paste0(substr(ISTESTCD, 1, 6), "LF")
    )
  )


# Update param_lookup dataset with your PARAM values.
param_lookup <- tribble(
  ~PARAMCD, ~PARAM, ~PARAMN,
  "J0033VN", "J0033VN Antibody", 1,
  "I0019NT", "I0019NT Antibody", 2,
  "M0019LN", "M0019LN Antibody", 3,
  "R0003MA", "R0003MA Antibody", 4,
  "J0033VNL", "LOG10 (J0033VN Antibody)", 11,
  "I0019NTL", "LOG10 (I0019NT Antibody)", 12,
  "M0019LNL", "LOG10 (M0019LN Antibody)", 13,
  "R0003MAL", "LOG10 (R0003MA Antibody)", 14,
  "J0033VNF", "4FOLD (J0033VN Antibody)", 21,
  "I0019NTF", "4FOLD (I0019NT Antibody)", 22,
  "M0019LNF", "4FOLD (M0019LN Antibody)", 23,
  "R0003MAF", "4FOLD (R0003MA Antibody)", 24,
  "J0033VLF", "LOG10 4FOLD (J0033VN Antibody)", 31,
  "I0019NLF", "LOG10 4FOLD (I0019NT Antibody)", 32,
  "M0019LLF", "LOG10 4FOLD (M0019LN Antibody)", 33,
  "R0003MLF", "LOG10 4FOLD (R0003MA Antibody)", 34
)

adis <- derive_vars_merged_lookup(
  dataset = adis,
  dataset_add = param_lookup,
  new_vars = exprs(PARAM, PARAMN),
  by_vars = exprs(PARAMCD)
)

dataset_vignette(
  adis,
  display_vars = exprs(USUBJID, VISITNUM, ISTEST, ISORRES, PARAMCD, PARAM, PARAMN)
)

Derive PARCAT1 and CUTOFFx Variables {#parcat}

Derive PARCAT1 and CUTOFFx variables.

Fake values has been put for CUTOFF values. Please, adapt base on your objectives.

adis <- adis %>%
  mutate(
    PARCAT1 = ISCAT,
    # Please, define your additional cutoff values. Delete if not needed.
    CUTOFF02 = 4,
    CUTOFF03 = 8
  )

dataset_vignette(
  adis,
  display_vars = exprs(USUBJID, VISITNUM, ISTEST, ISORRES, PARCAT1, CUTOFF02, CUTOFF03)
)

Derive AVAL, AVALU and DTYPE Variables {#aval}

This is the core of ADIS template.

For ORIGINAL (and relative log10 values) the following rule has been followed for AVAL derivation:

When the lab result (ISSTRESN) is below the Lower Limit Of Quantitation, then set ISSTRESN/2;
When the lab result (ISSTRESN) falls in the Lower Limit Of Quantitation and Upper Limit Of Quantitation range, then set ISSTRESN. If Upper Limit Of Quantitation is not present (not mapped into SDTM), then AVAL is equals to ISSTRESN when it is greater than Lower Limit Of Quantitation;
When the lab result (ISSTRESN) is greater then the Upper Limit Of Quantitation, then set to ISSTRESN. Upper_rule is an optional argument. If Upper Limit Of Quantitation is not present, you can remove it;

For 4fold (and relative log10 values) the rule is pretty the same, except when the LAB result (ISSTRESN) is lower than the Lower Limit Of Quantitation. In that case put ISSTRESN instead of ISSTRESN/2.

With log10 transformations, simply follow the before rules and apply log10 function.

Please, update this algorithm according to your Protocol/SAP.

AVALU is set equal to IS.ISSTRESU.

Later you can find SERCAT1/N and DTYPE derivations.

DTYPE is filled only for those records who exceed or are below the ISULOQ and ISSLOQ, respectively. If ISULOQ is not present, DTYPE is filled only when lab result is below Lower Limit of Quantitation.

adis_or <- adis %>%
  filter(DERIVED == "ORIG") %>%
  derive_var_aval_adis(
    lower_rule = ISLLOQ / 2,
    middle_rule = ISSTRESN,
    upper_rule = ISULOQ,
    round = 2
  )

adis_log_or <- adis %>%
  filter(DERIVED == "LOG10") %>%
  derive_var_aval_adis(
    lower_rule = log10(ISLLOQ / 2),
    middle_rule = log10(ISSTRESN),
    upper_rule = log10(ISULOQ),
    round = 2
  )

adis_4fold <- adis %>%
  filter(DERIVED == "4FOLD") %>%
  derive_var_aval_adis(
    lower_rule = ISLLOQ,
    middle_rule = ISSTRESN,
    upper_rule = ISULOQ,
    round = 2
  )

adis_log_4fold <- adis %>%
  filter(DERIVED == "LOG10 4FOLD") %>%
  derive_var_aval_adis(
    lower_rule = log10(ISLLOQ),
    middle_rule = log10(ISSTRESN),
    upper_rule = log10(ISULOQ),
    round = 2
  )

adis <- 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,

    # SERCAT1 derivation
    SERCAT1 = case_when(
      ISBLFL == "Y" & !is.na(AVAL) & !is.na(ISLLOQ) & AVAL < ISLLOQ ~ "S-",
      ISBLFL == "Y" & !is.na(AVAL) & !is.na(ISLLOQ) & AVAL >= ISLLOQ ~ "S+",
      ISBLFL == "Y" & (is.na(AVAL) | is.na(ISLLOQ)) ~ "UNKNOWN"
    )
  )


# Update param_lookup2 dataset with your SERCAT1N values.
param_lookup2 <- tribble(
  ~SERCAT1, ~SERCAT1N,
  "S-", 1,
  "S+", 2,
  "UNKNOWN", 3,
  NA_character_, NA_real_
)

adis <- derive_vars_merged_lookup(
  dataset = adis,
  dataset_add = param_lookup2,
  new_vars = exprs(SERCAT1N),
  by_vars = exprs(SERCAT1)
)


# DTYPE derivation.
# Please update code when <,<=,>,>= are present in your lab results (in ISSTRESC)

if (any(names(adis) == "ISULOQ") == TRUE) {
  adis <- adis %>%
    mutate(DTYPE = case_when(
      DERIVED %in% c("ORIG", "LOG10") & !is.na(ISLLOQ) &
        ((ISSTRESN < ISLLOQ) | grepl("<", ISORRES)) ~ "HALFLLOQ",
      DERIVED %in% c("ORIG", "LOG10") & !is.na(ISULOQ) &
        ((ISSTRESN > ISULOQ) | grepl(">", ISORRES)) ~ "ULOQ",
      TRUE ~ NA_character_
    ))
}

if (any(names(adis) == "ISULOQ") == FALSE) {
  adis <- adis %>%
    mutate(DTYPE = case_when(
      DERIVED %in% c("ORIG", "LOG10") & !is.na(ISLLOQ) &
        ((ISSTRESN < ISLLOQ) | grepl("<", ISORRES)) ~ "HALFLLOQ",
      TRUE ~ NA_character_
    ))
}

dataset_vignette(
  adis,
  display_vars = exprs(USUBJID, VISITNUM, ISTEST, ISORRES, AVAL, AVALU, DTYPE, SERCAT1, SERCAT1N)
)

Derive BASE Variables {#base}

Derive Baseline values for each Subject/Visit and relative flag, ABLFL.

In a later stage, derive BASECAT variable, which represents the base category. Update accordingly.

# ABLFL derivation
adis <- restrict_derivation(
  adis,
  derivation = derive_var_extreme_flag,
  args = params(
    by_vars = exprs(STUDYID, USUBJID, PARAMN),
    order = exprs(STUDYID, USUBJID, VISITNUM, PARAMN),
    new_var = ABLFL,
    mode = "first"
  ),
  filter = VISITNUM == 10
) %>%
  # BASE derivation
  derive_var_base(
    by_vars = exprs(STUDYID, USUBJID, PARAMN),
    source_var = AVAL,
    new_var = BASE,
    filter = ABLFL == "Y"
  ) %>%
  # BASETYPE derivation
  derive_basetype_records(
    basetypes = exprs("VISIT 1" = AVISITN %in% c(10, 30))
  ) %>%
  arrange(STUDYID, USUBJID, !is.na(DERIVED), ISSEQ)


# BASECAT derivation
adis <- adis %>%
  mutate(
    BASECAT1 = case_when(
      !grepl("L", PARAMCD) & BASE < 10 ~ "Titer value < 1:10",
      !grepl("L", PARAMCD) & BASE >= 10 ~ "Titer value >= 1:10",
      grepl("L", PARAMCD) & BASE < 10 ~ "Titer value < 1:10",
      grepl("L", PARAMCD) & BASE >= 10 ~ "Titer value >= 1:10"
    )
  )

dataset_vignette(
  adis,
  display_vars = exprs(USUBJID, VISITNUM, ISTEST, ISORRES, ABLFL, BASE, BASETYPE, BASECAT1)
)

Derive CHG and R2BASE Variables {#chg}

Derive change from baseline values.

Derive ratio to base values.

adis <- restrict_derivation(adis,
  derivation = derive_var_chg,
  filter = AVISITN > 10
) %>%
  restrict_derivation(
    derivation = derive_var_analysis_ratio,
    args = params(
      numer_var = AVAL,
      denom_var = BASE
    ),
    filter = AVISITN > 10
  ) %>%
  arrange(STUDYID, USUBJID, DERIVED, ISSEQ)

dataset_vignette(
  adis,
  display_vars = exprs(USUBJID, VISITNUM, ISTEST, ISORRES, CHG, R2BASE)
)

Derive CRITx Variables {#crit}

Derive Criteria Evaluation Analysis Flags.

The function selects a subset of rows from the input dataset and apply a criterion to them. If this criterion is met then CRIT1FL (or the name you specified in the first argument) is equal to Y; N otherwise.

The function returns a relative numeric CRIT1FN variable (1 or 0 if the criterion is met, respectively) and a label CRIT1 variable (with the text specified in label_var argument).

adis <- derive_vars_crit(
  dataset = adis,
  prefix = "CRIT1",
  crit_label = "Titer >= ISLLOQ",
  condition = !is.na(AVAL) & !is.na(ISLLOQ),
  criterion = AVAL >= ISLLOQ
)

dataset_vignette(
  adis,
  display_vars = exprs(USUBJID, VISITNUM, ISTEST, ISORRES, CRIT1, CRIT1FL, CRIT1FN)
)

Derive TRTP/A Variables {#trtp}

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

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

dataset_vignette(
  adis,
  display_vars = exprs(USUBJID, VISITNUM, ISTEST, ISORRES, TRTP, TRTA)
)

Derive PPS Record Level Flag Variable {#pps}

This is a record level flag which identifies which rows are included/excluded for the PPS related objectives.

This step could change according to your study needs.

adis <- adis %>%
  mutate(PPSRFL = if_else(VISITNUM %in% c(10, 30) & PPROTFL == "Y", "Y", NA_character_))

dataset_vignette(
  adis,
  display_vars = exprs(USUBJID, VISITNUM, ISTEST, ISORRES, TRTP, TRTA)
)

Add ADSL Variables {#adsl_vars}

Attach all ADAM.ADSL variables to the is build-in dataset.

If you may need to keep only a subset of them, please update accordingly.

# Get list of ADSL variables not to be added to ADIS
vx_adsl_vars <- exprs(RFSTDTC, PPROTFL)

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

dataset_vignette(
  adis,
  display_vars = exprs(USUBJID, VISITNUM, ISTEST, ISORRES, AGE, COUNTRY, ARM, ACTARM)
)

Example Script

ADaM | Sample Code ---- | -------------- ADIS | ad_adis.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.

rdrr.io home R language documentation Run R code online

CRAN packages Bioconductor packages R-Forge packages GitHub packages

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

Introduction

Programming Workflow

Read in Data {#readdata}

Combine IS with SUPPIS {#combine_supp}

Derive Timing Variables {#avisit}

Derive ADT and ADY Variables {#adt}

Parameters Derivation {#param}

Derive PARCAT1 and CUTOFFx Variables {#parcat}

Derive AVAL, AVALU and DTYPE Variables {#aval}

Derive BASE Variables {#base}

Derive CHG and R2BASE Variables {#chg}

Derive CRITx Variables {#crit}

Derive TRTP/A Variables {#trtp}

Derive PPS Record Level Flag Variable {#pps}

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

Introduction

Programming Workflow

Read in Data {#readdata}

Combine IS with SUPPIS {#combine_supp}

Derive Timing Variables {#avisit}

Derive ADT and ADY Variables {#adt}

Parameters Derivation {#param}

Derive PARCAT1 and CUTOFFx Variables {#parcat}

Derive AVAL, AVALU and DTYPE Variables {#aval}

Derive BASE Variables {#base}

Derive CHG and R2BASE Variables {#chg}

Derive CRITx Variables {#crit}

Derive TRTP/A Variables {#trtp}

Derive PPS Record Level Flag Variable {#pps}

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