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Creating ADIS

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

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 IS with its supplemental domain SUPPIS.

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

Derive Timing Variables

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_
    )
  )
USUBJID VISITNUM ISTEST ISORRES AVISIT AVISITN ATPT ATPTN ATPTREF
ABC-1001 10 J0033VN Antibody NA Visit 1 10 Visit 1 (Day 1) 1 FIRST TREATMENT
ABC-1001 10 I0019NT Antibody 3 Visit 1 10 Visit 1 (Day 1) 1 FIRST TREATMENT
ABC-1001 10 M0019LN Antibody >150 Visit 1 10 Visit 1 (Day 1) 1 FIRST TREATMENT
ABC-1001 10 R0003MA Antibody 140.5 Visit 1 10 Visit 1 (Day 1) 1 FIRST TREATMENT
ABC-1001 30 J0033VN Antibody 2 Visit 3 30 Visit 3 (Day 61) 3 SECOND TREATMENT
ABC-1001 30 I0019NT Antibody >200 Visit 3 30 Visit 3 (Day 61) 3 SECOND TREATMENT
ABC-1001 30 M0019LN Antibody <2 Visit 3 30 Visit 3 (Day 61) 3 SECOND TREATMENT
ABC-1001 30 R0003MA Antibody 98.2 Visit 3 30 Visit 3 (Day 61) 3 SECOND TREATMENT
ABC-1002 10 J0033VN Antibody 3 Visit 1 10 Visit 1 (Day 1) 1 FIRST TREATMENT
ABC-1002 10 I0019NT Antibody NA Visit 1 10 Visit 1 (Day 1) 1 FIRST TREATMENT

Derive ADT and ADY Variables

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

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)
  )
USUBJID VISITNUM ISTEST ISORRES ISDTC RFSTDTC ADT ADY PPROTFL
ABC-1001 10 J0033VN Antibody NA NA 2021-11-03 NA NA Y
ABC-1001 10 I0019NT Antibody 3 2021-11 2021-11-03 2021-11-15 13 Y
ABC-1001 10 M0019LN Antibody >150 2021-11 2021-11-03 2021-11-15 13 Y
ABC-1001 10 R0003MA Antibody 140.5 2021-11 2021-11-03 2021-11-15 13 Y
ABC-1001 30 J0033VN Antibody 2 2021-12 2021-11-03 2021-12-15 43 Y
ABC-1001 30 I0019NT Antibody >200 2021-12 2021-11-03 2021-12-15 43 Y
ABC-1001 30 M0019LN Antibody <2 2021-12 2021-11-03 2021-12-15 43 Y
ABC-1001 30 R0003MA Antibody 98.2 2021-12 2021-11-03 2021-12-15 43 Y
ABC-1002 10 J0033VN Antibody 3 2021 2021-10-07 2021-06-30 -99 Y
ABC-1002 10 I0019NT Antibody NA NA 2021-10-07 NA NA Y

Parameters Derivation

In this template, duplicated records for PARAMCD have been created. In particular, you may find 4 different parameters 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)
)
#> All `PARAMCD` are mapped.
USUBJID VISITNUM ISTEST ISORRES PARAMCD PARAM PARAMN
ABC-1001 10 J0033VN Antibody NA J0033VN J0033VN Antibody 1
ABC-1001 10 I0019NT Antibody 3 I0019NT I0019NT Antibody 2
ABC-1001 10 M0019LN Antibody >150 M0019LN M0019LN Antibody 3
ABC-1001 10 R0003MA Antibody 140.5 R0003MA R0003MA Antibody 4
ABC-1001 30 J0033VN Antibody 2 J0033VN J0033VN Antibody 1
ABC-1001 30 I0019NT Antibody >200 I0019NT I0019NT Antibody 2
ABC-1001 30 M0019LN Antibody <2 M0019LN M0019LN Antibody 3
ABC-1001 30 R0003MA Antibody 98.2 R0003MA R0003MA Antibody 4
ABC-1001 10 J0033VN Antibody NA J0033VNL LOG10 (J0033VN Antibody) 11
ABC-1001 10 I0019NT Antibody 3 I0019NTL LOG10 (I0019NT Antibody) 12

Derive PARCAT1 and CUTOFFx Variables

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
  )
USUBJID VISITNUM ISTEST ISORRES PARCAT1 CUTOFF02 CUTOFF03
ABC-1001 10 J0033VN Antibody NA IMMUNOLOGY 4 8
ABC-1001 10 I0019NT Antibody 3 IMMUNOLOGY 4 8
ABC-1001 10 M0019LN Antibody >150 IMMUNOLOGY 4 8
ABC-1001 10 R0003MA Antibody 140.5 IMMUNOLOGY 4 8
ABC-1001 30 J0033VN Antibody 2 IMMUNOLOGY 4 8
ABC-1001 30 I0019NT Antibody >200 IMMUNOLOGY 4 8
ABC-1001 30 M0019LN Antibody <2 IMMUNOLOGY 4 8
ABC-1001 30 R0003MA Antibody 98.2 IMMUNOLOGY 4 8
ABC-1001 10 J0033VN Antibody NA IMMUNOLOGY 4 8
ABC-1001 10 I0019NT Antibody 3 IMMUNOLOGY 4 8

Derive AVAL, AVALU and DTYPE Variables

This is the core of ADIS template.

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

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)
)
#> All `SERCAT1` are mapped.


# 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_
    ))
}
USUBJID VISITNUM ISTEST ISORRES AVAL AVALU DTYPE SERCAT1 SERCAT1N
ABC-1001 10 J0033VN Antibody NA NA NA NA UNKNOWN 3
ABC-1001 10 I0019NT Antibody 3 2.0 titer HALFLLOQ S- 1
ABC-1001 10 M0019LN Antibody >150 150.0 titer ULOQ S+ 2
ABC-1001 10 R0003MA Antibody 140.5 120.0 titer ULOQ S+ 2
ABC-1001 30 J0033VN Antibody 2 2.0 titer NA NA NA
ABC-1001 30 I0019NT Antibody >200 200.0 titer ULOQ NA NA
ABC-1001 30 M0019LN Antibody <2 4.0 titer HALFLLOQ NA NA
ABC-1001 30 R0003MA Antibody 98.2 98.2 titer NA NA NA
ABC-1002 10 J0033VN Antibody 3 3.0 titer NA S+ 2
ABC-1002 10 I0019NT Antibody NA NA NA NA UNKNOWN 3

Derive BASE Variables

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"
    )
  )
USUBJID VISITNUM ISTEST ISORRES ABLFL BASE BASETYPE BASECAT1
ABC-1001 10 J0033VN Antibody NA Y NA VISIT 1 NA
ABC-1001 10 I0019NT Antibody 3 Y 2.0 VISIT 1 Titer value < 1:10
ABC-1001 10 M0019LN Antibody >150 Y 150.0 VISIT 1 Titer value >= 1:10
ABC-1001 10 R0003MA Antibody 140.5 Y 120.0 VISIT 1 Titer value >= 1:10
ABC-1001 30 J0033VN Antibody 2 NA NA VISIT 1 NA
ABC-1001 30 I0019NT Antibody >200 NA 2.0 VISIT 1 Titer value < 1:10
ABC-1001 30 M0019LN Antibody <2 NA 150.0 VISIT 1 Titer value >= 1:10
ABC-1001 30 R0003MA Antibody 98.2 NA 120.0 VISIT 1 Titer value >= 1:10
ABC-1001 10 J0033VN Antibody NA Y NA VISIT 1 NA
ABC-1001 10 I0019NT Antibody 3 Y 0.3 VISIT 1 Titer value < 1:10

Derive CHG and R2BASE Variables

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)
USUBJID VISITNUM ISTEST ISORRES CHG R2BASE
ABC-1001 30 J0033VN Antibody 2 NA NA
ABC-1001 30 I0019NT Antibody >200 196.0 50.0000000
ABC-1001 30 M0019LN Antibody <2 -142.0 0.0533333
ABC-1001 30 R0003MA Antibody 98.2 -21.8 0.8183333
ABC-1001 10 J0033VN Antibody NA NA NA
ABC-1001 10 I0019NT Antibody 3 NA NA
ABC-1001 10 M0019LN Antibody >150 NA NA
ABC-1001 10 R0003MA Antibody 140.5 NA NA
ABC-1001 30 J0033VN Antibody 2 NA NA
ABC-1001 30 I0019NT Antibody >200 2.0 7.6666667

Derive CRITx Variables

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
)
USUBJID VISITNUM ISTEST ISORRES CRIT1 CRIT1FL CRIT1FN
ABC-1001 30 J0033VN Antibody 2 Titer >= ISLLOQ Y 1
ABC-1001 30 I0019NT Antibody >200 Titer >= ISLLOQ Y 1
ABC-1001 30 M0019LN Antibody <2 Titer >= ISLLOQ Y 1
ABC-1001 30 R0003MA Antibody 98.2 Titer >= ISLLOQ Y 1
ABC-1001 10 J0033VN Antibody NA NA NA NA
ABC-1001 10 I0019NT Antibody 3 Titer >= ISLLOQ Y 1
ABC-1001 10 M0019LN Antibody >150 Titer >= ISLLOQ Y 1
ABC-1001 10 R0003MA Antibody 140.5 Titer >= ISLLOQ Y 1
ABC-1001 30 J0033VN Antibody 2 Titer >= ISLLOQ N 0
ABC-1001 30 I0019NT Antibody >200 Titer >= ISLLOQ N 0

Derive TRTP/A Variables

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"
)
USUBJID VISITNUM ISTEST ISORRES TRTP TRTA
ABC-1001 30 J0033VN Antibody 2 VACCINE A VACCINE A
ABC-1001 30 I0019NT Antibody >200 VACCINE A VACCINE A
ABC-1001 30 M0019LN Antibody <2 VACCINE A VACCINE A
ABC-1001 30 R0003MA Antibody 98.2 VACCINE A VACCINE A
ABC-1001 10 J0033VN Antibody NA NA NA
ABC-1001 10 I0019NT Antibody 3 VACCINE A VACCINE A
ABC-1001 10 M0019LN Antibody >150 VACCINE A VACCINE A
ABC-1001 10 R0003MA Antibody 140.5 VACCINE A VACCINE A
ABC-1001 30 J0033VN Antibody 2 VACCINE A VACCINE A
ABC-1001 30 I0019NT Antibody >200 VACCINE A VACCINE A

Derive PPS Record Level Flag Variable

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_))
USUBJID VISITNUM ISTEST ISORRES TRTP TRTA
ABC-1001 30 J0033VN Antibody 2 VACCINE A VACCINE A
ABC-1001 30 I0019NT Antibody >200 VACCINE A VACCINE A
ABC-1001 30 M0019LN Antibody <2 VACCINE A VACCINE A
ABC-1001 30 R0003MA Antibody 98.2 VACCINE A VACCINE A
ABC-1001 10 J0033VN Antibody NA NA NA
ABC-1001 10 I0019NT Antibody 3 VACCINE A VACCINE A
ABC-1001 10 M0019LN Antibody >150 VACCINE A VACCINE A
ABC-1001 10 R0003MA Antibody 140.5 VACCINE A VACCINE A
ABC-1001 30 J0033VN Antibody 2 VACCINE A VACCINE A
ABC-1001 30 I0019NT Antibody >200 VACCINE A VACCINE A

Add ADSL Variables

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")
)
USUBJID VISITNUM ISTEST ISORRES AGE COUNTRY ARM ACTARM
ABC-1001 30 J0033VN Antibody 2 74 USA VACCINE A VACCINE B VACCINE A VACCINE B
ABC-1001 30 I0019NT Antibody >200 74 USA VACCINE A VACCINE B VACCINE A VACCINE B
ABC-1001 30 M0019LN Antibody <2 74 USA VACCINE A VACCINE B VACCINE A VACCINE B
ABC-1001 30 R0003MA Antibody 98.2 74 USA VACCINE A VACCINE B VACCINE A VACCINE B
ABC-1001 10 J0033VN Antibody NA 74 USA VACCINE A VACCINE B VACCINE A VACCINE B
ABC-1001 10 I0019NT Antibody 3 74 USA VACCINE A VACCINE B VACCINE A VACCINE B
ABC-1001 10 M0019LN Antibody >150 74 USA VACCINE A VACCINE B VACCINE A VACCINE B
ABC-1001 10 R0003MA Antibody 140.5 74 USA VACCINE A VACCINE B VACCINE A VACCINE B
ABC-1001 30 J0033VN Antibody 2 74 USA VACCINE A VACCINE B VACCINE A VACCINE B
ABC-1001 30 I0019NT Antibody >200 74 USA VACCINE A VACCINE B VACCINE A VACCINE B

Example Script

ADaM Sample Code
ADIS ad_adis.R

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.