Create dataset of single doses

Derives dataset of single dose from aggregate dose information. This may be necessary when e.g. calculating last dose before an adverse event in ADAE or deriving a total dose parameter in ADEX when EXDOSFRQ != ONCE.

Usage

create_single_dose_dataset(
  dataset,
  dose_freq = EXDOSFRQ,
  start_date = ASTDT,
  start_datetime = NULL,
  end_date = AENDT,
  end_datetime = NULL,
  lookup_table = dose_freq_lookup,
  lookup_column = CDISC_VALUE,
  nominal_time = NULL,
  keep_source_vars = expr_c(get_admiral_option("subject_keys"), dose_freq, start_date,
    start_datetime, end_date, end_datetime)
)

Arguments

dataset

Input dataset

The variables specified by the dose_freq, start_date, and end_date arguments are expected to be in the dataset.

dose_freq

The dose frequency

The aggregate dosing frequency used for multiple doses in a row.

Permitted Values: defined by lookup table.

start_date

The start date

A date object is expected. This object cannot contain NA values.

Refer to derive_vars_dt() to impute and derive a date from a date character vector to a date object.

start_datetime

The start date-time

A date-time object is expected. This object cannot contain NA values.

Refer to derive_vars_dtm() to impute and derive a date-time from a date character vector to a date object.

If the input dataset contains frequencies which refer to DOSE_WINDOW equals "HOUR" or "MINUTE", the parameter must be specified.

end_date

The end date

A date or date-time object is expected. This object cannot contain NA values.

Refer to derive_vars_dt() to impute and derive a date from a date character vector to a date object.

end_datetime

The end date-time

A date-time object is expected. This object cannot contain NA values.

Refer to derive_vars_dtm() to impute and derive a date-time from a date character vector to a date object.

If the input dataset contains frequencies which refer to DOSE_WINDOW equals "HOUR" or "MINUTE", the parameter must be specified.

lookup_table

The dose frequency value lookup table

The table used to look up dose_freq values and determine the appropriate multiplier to be used for row generation. If a lookup table other than the default is used, it must have columns DOSE_WINDOW, DOSE_COUNT, and CONVERSION_FACTOR. The default table dose_freq_lookup is described in detail here.

Permitted Values for DOSE_WINDOW: "MINUTE", "HOUR", "DAY", "WEEK", "MONTH", "YEAR"

lookup_column

The dose frequency value column in the lookup table

The column of lookup_table.

nominal_time

The nominal relative time from first dose (NFRLT)

Used for PK analysis, this will be in hours and should be 0 for the first dose. It can be derived as (VISITDY - 1) * 24 for example. This will be expanded as the single dose dataset is created. For example an EXDOFRQ of "QD" will result in the nominal_time being incremented by 24 hours for each expanded record.

The value can be NULL if not needed.

keep_source_vars

List of variables to be retained from source dataset

This parameter can be specified if additional information is required in the output dataset. For example EXTRT for studies with more than one drug.

Value

The input dataset with a single dose per row.

Details

Each aggregate dose row is split into multiple rows which each represent a single dose.The number of completed dose periods between start_date or start_datetime and end_date or end_datetime is calculated with compute_duration and multiplied by DOSE_COUNT. For DOSE_WINDOW values of "WEEK", "MONTH", and "YEAR", CONVERSION_FACTOR is used to convert into days the time object to be added to start_date.

Observations with dose frequency "ONCE" are copied to the output dataset unchanged.

Examples

# Example with default lookup

library(lubridate)
library(stringr)
library(tibble)
library(dplyr)

data <- tribble(
  ~STUDYID, ~USUBJID, ~EXDOSFRQ, ~ASTDT, ~ASTDTM, ~AENDT, ~AENDTM,
  "STUDY01", "P01", "Q2D", ymd("2021-01-01"), ymd_hms("2021-01-01 10:30:00"),
  ymd("2021-01-07"), ymd_hms("2021-01-07 11:30:00"),
  "STUDY01", "P01", "Q3D", ymd("2021-01-08"), ymd_hms("2021-01-08 12:00:00"),
  ymd("2021-01-14"), ymd_hms("2021-01-14 14:00:00"),
  "STUDY01", "P01", "EVERY 2 WEEKS", ymd("2021-01-15"), ymd_hms("2021-01-15 09:57:00"),
  ymd("2021-01-29"), ymd_hms("2021-01-29 10:57:00")
)

create_single_dose_dataset(data)
#> # A tibble: 9 × 5
#>   STUDYID USUBJID EXDOSFRQ ASTDT      AENDT     
#>   <chr>   <chr>   <chr>    <date>     <date>    
#> 1 STUDY01 P01     ONCE     2021-01-01 2021-01-01
#> 2 STUDY01 P01     ONCE     2021-01-03 2021-01-03
#> 3 STUDY01 P01     ONCE     2021-01-05 2021-01-05
#> 4 STUDY01 P01     ONCE     2021-01-07 2021-01-07
#> 5 STUDY01 P01     ONCE     2021-01-08 2021-01-08
#> 6 STUDY01 P01     ONCE     2021-01-11 2021-01-11
#> 7 STUDY01 P01     ONCE     2021-01-14 2021-01-14
#> 8 STUDY01 P01     ONCE     2021-01-15 2021-01-15
#> 9 STUDY01 P01     ONCE     2021-01-29 2021-01-29

# Example with custom lookup

custom_lookup <- tribble(
  ~Value,   ~DOSE_COUNT, ~DOSE_WINDOW, ~CONVERSION_FACTOR,
  "Q30MIN", (1 / 30),    "MINUTE",                      1,
  "Q90MIN", (1 / 90),    "MINUTE",                      1
)

data <- tribble(
  ~STUDYID, ~USUBJID, ~EXDOSFRQ, ~ASTDT, ~ASTDTM, ~AENDT, ~AENDTM,
  "STUDY01", "P01", "Q30MIN", ymd("2021-01-01"), ymd_hms("2021-01-01T06:00:00"),
  ymd("2021-01-01"), ymd_hms("2021-01-01T07:00:00"),
  "STUDY02", "P02", "Q90MIN", ymd("2021-01-01"), ymd_hms("2021-01-01T06:00:00"),
  ymd("2021-01-01"), ymd_hms("2021-01-01T09:00:00")
)

create_single_dose_dataset(data,
  lookup_table = custom_lookup,
  lookup_column = Value,
  start_datetime = ASTDTM,
  end_datetime = AENDTM
)
#> # A tibble: 6 × 7
#>   STUDYID USUBJID EXDOSFRQ ASTDT      ASTDTM              AENDT     
#>   <chr>   <chr>   <chr>    <date>     <dttm>              <date>    
#> 1 STUDY01 P01     ONCE     2021-01-01 2021-01-01 06:00:00 2021-01-01
#> 2 STUDY01 P01     ONCE     2021-01-01 2021-01-01 06:30:00 2021-01-01
#> 3 STUDY01 P01     ONCE     2021-01-01 2021-01-01 07:00:00 2021-01-01
#> 4 STUDY02 P02     ONCE     2021-01-01 2021-01-01 06:00:00 2021-01-01
#> 5 STUDY02 P02     ONCE     2021-01-01 2021-01-01 07:30:00 2021-01-01
#> 6 STUDY02 P02     ONCE     2021-01-01 2021-01-01 09:00:00 2021-01-01
#> # ℹ 1 more variable: AENDTM <dttm>
# Example with nominal time

data <- tribble(
  ~STUDYID, ~USUBJID, ~EXDOSFRQ, ~NFRLT, ~ASTDT, ~ASTDTM, ~AENDT, ~AENDTM,
  "STUDY01", "P01", "BID", 0, ymd("2021-01-01"), ymd_hms("2021-01-01 08:00:00"),
  ymd("2021-01-07"), ymd_hms("2021-01-07 20:00:00"),
  "STUDY01", "P01", "BID", 168, ymd("2021-01-08"), ymd_hms("2021-01-08 08:00:00"),
  ymd("2021-01-14"), ymd_hms("2021-01-14 20:00:00"),
  "STUDY01", "P01", "BID", 336, ymd("2021-01-15"), ymd_hms("2021-01-15 08:00:00"),
  ymd("2021-01-29"), ymd_hms("2021-01-29 20:00:00")
)

create_single_dose_dataset(data,
  dose_freq = EXDOSFRQ,
  start_date = ASTDT,
  start_datetime = ASTDTM,
  end_date = AENDT,
  end_datetime = AENDTM,
  lookup_table = dose_freq_lookup,
  lookup_column = CDISC_VALUE,
  nominal_time = NFRLT,
  keep_source_vars = exprs(
    USUBJID, EXDOSFRQ, ASTDT, ASTDTM, AENDT, AENDTM, NFRLT
  )
)
#> # A tibble: 58 × 7
#>    USUBJID EXDOSFRQ ASTDT      ASTDTM              AENDT     
#>    <chr>   <chr>    <date>     <dttm>              <date>    
#>  1 P01     ONCE     2021-01-01 2021-01-01 08:00:00 2021-01-01
#>  2 P01     ONCE     2021-01-01 2021-01-01 20:00:00 2021-01-01
#>  3 P01     ONCE     2021-01-02 2021-01-02 08:00:00 2021-01-02
#>  4 P01     ONCE     2021-01-02 2021-01-02 20:00:00 2021-01-02
#>  5 P01     ONCE     2021-01-03 2021-01-03 08:00:00 2021-01-03
#>  6 P01     ONCE     2021-01-03 2021-01-03 20:00:00 2021-01-03
#>  7 P01     ONCE     2021-01-04 2021-01-04 08:00:00 2021-01-04
#>  8 P01     ONCE     2021-01-04 2021-01-04 20:00:00 2021-01-04
#>  9 P01     ONCE     2021-01-05 2021-01-05 08:00:00 2021-01-05
#> 10 P01     ONCE     2021-01-05 2021-01-05 20:00:00 2021-01-05
#> # ℹ 48 more rows
#> # ℹ 2 more variables: AENDTM <dttm>, NFRLT <dbl>

# Example - derive a single dose dataset with imputations

# For either single drug administration records, or multiple drug administration
# records covering a range of dates, fill-in of missing treatment end datetime
# `EXENDTC` by substitution with an acceptable alternate, for example date of
# death, date of datacut may be required. This example shows the
# maximum possible number of single dose records to be derived. The example
# requires the date of datacut `DCUTDT` to be specified correctly, or
# if not appropriate to use `DCUTDT` as missing treatment end data and missing
# treatment end datetime could set equal to treatment start date and treatment
# start datetime. ADSL variables `DTHDT` and `DCUTDT` are preferred for
# imputation use.
#
# All available trial treatments are included, allowing multiple different
# last dose variables to be created in for example `use_ad_template("ADAE")`
# if required.

adsl <- tribble(
  ~STUDYID, ~USUBJID, ~DTHDT,
  "01", "1211", ymd("2013-01-14"),
  "01", "1083", ymd("2013-08-02"),
  "01", "1445", ymd("2014-11-01"),
  "01", "1015", NA,
  "01", "1023", NA
)

ex <- tribble(
  ~STUDYID, ~USUBJID, ~EXSEQ, ~EXTRT, ~EXDOSE, ~EXDOSU, ~EXDOSFRQ, ~EXSTDTC, ~EXENDTC,
  "01", "1015", 1, "PLAC", 0, "mg", "QD", "2014-01-02", "2014-01-16",
  "01", "1015", 2, "PLAC", 0, "mg", "QD", "2014-06-17", "2014-06-18",
  "01", "1015", 3, "PLAC", 0, "mg", "QD", "2014-06-19", NA_character_,
  "01", "1023", 1, "PLAC", 0, "mg", "QD", "2012-08-05", "2012-08-27",
  "01", "1023", 2, "PLAC", 0, "mg", "QD", "2012-08-28", "2012-09-01",
  "01", "1211", 1, "XANO", 54, "mg", "QD", "2012-11-15", "2012-11-28",
  "01", "1211", 2, "XANO", 54, "mg", "QD", "2012-11-29", NA_character_,
  "01", "1445", 1, "PLAC", 0, "mg", "QD", "2014-05-11", "2014-05-25",
  "01", "1445", 2, "PLAC", 0, "mg", "QD", "2014-05-26", "2014-11-01",
  "01", "1083", 1, "PLAC", 0, "mg", "QD", "2013-07-22", "2013-08-01"
)

adsl_death <- adsl %>%
  mutate(
    DTHDTM = convert_date_to_dtm(DTHDT),
    # Remove `DCUT` setup line below if ADSL `DCUTDT` is populated.
    DCUTDT = convert_dtc_to_dt("2015-03-06"), # Example only, enter date.
    DCUTDTM = convert_date_to_dtm(DCUTDT)
  )

# Select valid dose records, non-missing `EXSTDTC` and `EXDOSE`.
ex_mod <- ex %>%
  filter(!is.na(EXSTDTC) & !is.na(EXDOSE)) %>%
  derive_vars_merged(adsl_death, by_vars = get_admiral_option("subject_keys")) %>%
  # Example, set up missing `EXDOSFRQ` as QD daily dosing regime.
  # Replace with study dosing regime per trial treatment.
  mutate(EXDOSFRQ = if_else(is.na(EXDOSFRQ), "QD", EXDOSFRQ)) %>%
  # Create EXxxDTM variables and replace missing `EXENDTM`.
  derive_vars_dtm(
    dtc = EXSTDTC,
    new_vars_prefix = "EXST",
    date_imputation = "first",
    time_imputation = "first",
    flag_imputation = "none",
  ) %>%
  derive_vars_dtm_to_dt(exprs(EXSTDTM)) %>%
  derive_vars_dtm(
    dtc = EXENDTC,
    new_vars_prefix = "EXEN",
    # Maximum imputed treatment end date must not be not greater than
    # date of death or after the datacut date.
    max_dates = exprs(DTHDTM, DCUTDTM),
    date_imputation = "last",
    time_imputation = "last",
    flag_imputation = "none",
    highest_imputation = "Y",
  ) %>%
  derive_vars_dtm_to_dt(exprs(EXENDTM)) %>%
  # Select only unique values.
  # Removes duplicated records before final step.
  distinct(
    STUDYID, USUBJID, EXTRT, EXDOSE, EXDOSFRQ, DCUTDT, DTHDT, EXSTDT,
    EXSTDTM, EXENDT, EXENDTM, EXSTDTC, EXENDTC
  )

create_single_dose_dataset(
  ex_mod,
  start_date = EXSTDT,
  start_datetime = EXSTDTM,
  end_date = EXENDT,
  end_datetime = EXENDTM,
  keep_source_vars = exprs(
    STUDYID, USUBJID, EXTRT, EXDOSE, EXDOSFRQ,
    DCUTDT, EXSTDT, EXSTDTM, EXENDT, EXENDTM, EXSTDTC, EXENDTC
  )
)
#> # A tibble: 553 × 12
#>    STUDYID USUBJID EXTRT EXDOSE EXDOSFRQ DCUTDT     EXSTDT    
#>    <chr>   <chr>   <chr>  <dbl> <chr>    <date>     <date>    
#>  1 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-02
#>  2 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-03
#>  3 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-04
#>  4 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-05
#>  5 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-06
#>  6 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-07
#>  7 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-08
#>  8 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-09
#>  9 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-10
#> 10 01      1015    PLAC       0 ONCE     2015-03-06 2014-01-11
#> # ℹ 543 more rows
#> # ℹ 5 more variables: EXSTDTM <dttm>, EXENDT <date>, EXENDTM <dttm>,
#> #   EXSTDTC <chr>, EXENDTC <chr>

Usage

Arguments

Value

Details

See also

Examples