derive_extreme_records: Add the First or Last Observation for Each By Group as New...

In Roche-GSK/admiral: ADaM in R Asset Library

Add the First or Last Observation for Each By Group as New Records

Description

Add the first or last observation for each by group as new observations. The new observations can be selected from the additional dataset. This function can be used for adding the maximum or minimum value as a separate visit. All variables of the selected observation are kept. This distinguishes derive_extreme_records() from derive_summary_records(), where only the by variables are populated for the new records.

Usage

derive_extreme_records(
  dataset = NULL,
  dataset_add,
  dataset_ref = NULL,
  by_vars = NULL,
  order = NULL,
  mode = NULL,
  filter_add = NULL,
  check_type = "warning",
  exist_flag = NULL,
  true_value = "Y",
  false_value = NA_character_,
  keep_source_vars = exprs(everything()),
  set_values_to
)

Arguments

dataset	Input dataset If the argument is not specified (or set to NULL), a new dataset is created. Otherwise, the new records are appended to the specified dataset. Permitted values a dataset, i.e., a data.frame or tibble Default value NULL
dataset_add	Additional dataset The additional dataset, which determines the by groups returned in the input dataset, based on the groups that exist in this dataset after being subset by filter_add. The variables specified in the by_vars and filter_add parameters are expected in this dataset. If mode and order are specified, the first or last observation within each by group, defined by by_vars, is selected. Permitted values a dataset, i.e., a data.frame or tibble Default value none
dataset_ref	Reference dataset The variables specified for by_vars are expected. For each observation of the specified dataset a new observation is added to the input dataset. For records which are added from dataset_ref because there are no records in dataset_add for the by group only those variables are kept which are also in dataset_add (and are included in keep_source_vars). Permitted values a dataset, i.e., a data.frame or tibble Default value NULL
by_vars	Grouping variables If dataset_ref is specified, this argument must be specified. Permitted values list of variables created by exprs(), e.g., exprs(USUBJID, VISIT) Default value NULL
order	Sort order Within each by group the observations are ordered by the specified order. Permitted values list of expressions created by exprs(), e.g., exprs(ADT, desc(AVAL)) Default value none
mode	Selection mode (first or last) If "first" is specified, the first observation of each by group is added to the input dataset. If "last" is specified, the last observation of each by group is added to the input dataset. Permitted values "first", "last" Default value NULL
filter_add	Filter for additional dataset (dataset_add) Only observations in dataset_add fulfilling the specified condition are considered. Permitted values an unquoted condition, e.g., AVISIT == "BASELINE" Default value NULL
check_type	Check uniqueness? If "warning" or "error" is specified, the specified message is issued if the observations of the (restricted) additional dataset are not unique with respect to the by variables and the order. Permitted values "none", "message", "warning", "error" Default value "warning"
exist_flag	Existence flag The specified variable is added to the output dataset. For by groups with at least one observation in the additional dataset (dataset_add) exist_flag is set to the value specified by the true_value argument. For all other by groups exist_flag is set to the value specified by the false_value argument. Permitted values Variable name Default value NULL
true_value	True value For new observations selected from the additional dataset (dataset_add), exist_flag is set to the specified value. Default value "Y"
false_value	False value For new observations not selected from the additional dataset (dataset_add), exist_flag is set to the specified value. Default value NA_character_
keep_source_vars	Variables to be kept in the new records A named list or tidyselect expressions created by exprs() defining the variables to be kept for the new records. The variables specified for by_vars and set_values_to need not be specified here as they are kept automatically. Default value exprs(everything())
set_values_to	Variables to be set The specified variables are set to the specified values for the new observations. Set a list of variables to some specified value for the new records LHS refer to a variable. RHS refers to the values to set to the variable. This can be a string, a symbol, a numeric value, an expression or NA. If summary functions are used, the values are summarized by the variables specified for by_vars. For example: set_values_to = exprs( AVAL = sum(AVAL), DTYPE = "AVERAGE", ) Default value none

Details

1. The additional dataset (dataset_add) is restricted as specified by the filter_add argument.

2. For each group (with respect to the variables specified for the by_vars argument) the first or last observation (with respect to the order specified for the order argument and the mode specified for the mode argument) is selected.

3. If dataset_ref is specified, observations which are in dataset_ref but not in the selected records are added.

4. The variables specified by the set_values_to argument are added to the selected observations.

5. The variables specified by the keep_source_vars argument are selected along with the variables specified in by_vars and set_values_to arguments.

6. The observations are added to input dataset (dataset). If no input dataset is provided, a new dataset is created.

Value

The input dataset with the first or last observation of each by group added as new observations.

Examples

Add last/first record as new record

For each subject the last record should be added as a new visit.

• The source dataset for the new records is specified by the dataset_add argument. Here it is the same as the input dataset.

• The groups for which new records are added are specified by the by_vars argument. Here for each subject a record should be added. Thus by_vars = exprs(USUBJID) is specified.

• As there are multiple records per subject, the mode and order arguments are specified to request that the last record is selected when the records are sorted by visit (AVISITN). The records are sorted by each by group (by_vars) separately, i.e., it is not necessary to add the variables from by_vars to order.

• To avoid duplicates in the output dataset the set_values_to argument is specified to set the visit (AVISIT) to a special value for the new records.

library(tibble)
library(dplyr, warn.conflicts = FALSE)
library(lubridate, warn.conflicts = FALSE)

adlb <- tribble(
  ~USUBJID, ~AVISITN, ~AVAL,
  "1",      1,          113,
  "1",      2,          111,
  "2",      1,          101,
  "2",      2,           NA,
  "3",      1,           NA,
)

derive_extreme_records(
  adlb,
  dataset_add = adlb,
  by_vars = exprs(USUBJID),
  order = exprs(AVISITN),
  mode = "last",
  set_values_to = exprs(
    AVISITN = 99
  )
)
#> # A tibble: 8 × 3
#>   USUBJID AVISITN  AVAL
#>   <chr>     <dbl> <dbl>
#> 1 1             1   113
#> 2 1             2   111
#> 3 2             1   101
#> 4 2             2    NA
#> 5 3             1    NA
#> 6 1            99   111
#> 7 2            99    NA
#> 8 3            99    NA

Restricting source records (filter_add)

The source records can be restricted by the filter_add argument, e.g., to exclude visits with missing analysis value from selecting for the last visit record:

derive_extreme_records(
  adlb,
  dataset_add = adlb,
  filter_add = !is.na(AVAL),
  by_vars = exprs(USUBJID),
  order = exprs(AVISITN),
  mode = "last",
  set_values_to = exprs(
    AVISITN = 99
  )
)
#> # A tibble: 7 × 3
#>   USUBJID AVISITN  AVAL
#>   <chr>     <dbl> <dbl>
#> 1 1             1   113
#> 2 1             2   111
#> 3 2             1   101
#> 4 2             2    NA
#> 5 3             1    NA
#> 6 1            99   111
#> 7 2            99   101

Please note that new records are added only for subjects in the restricted source data. Therefore no new record is added for subject "3".

Adding records for groups not in source (dataset_ref)

Adding records for groups which are not in the source data can be achieved by specifying a reference dataset by the dataset_ref argument. For example, specifying the input dataset for dataset_ref below ensures that new records are added also for subject without a valid analysis value:

derive_extreme_records(
  adlb,
  dataset_add = adlb,
  filter_add = !is.na(AVAL),
  dataset_ref = adlb,
  by_vars = exprs(USUBJID),
  order = exprs(AVISITN),
  mode = "last",
  set_values_to = exprs(
    AVISITN = 99
  )
)
#> # A tibble: 8 × 3
#>   USUBJID AVISITN  AVAL
#>   <chr>     <dbl> <dbl>
#> 1 1             1   113
#> 2 1             2   111
#> 3 2             1   101
#> 4 2             2    NA
#> 5 3             1    NA
#> 6 1            99   111
#> 7 2            99   101
#> 8 3            99    NA

Selecting variables for new records (keep_source_vars)

Which variables from the source dataset are kept for the new records can be specified by the keep_source_vars argument. Variables specified by by_vars or set_values_to don't need to be added to keep_source_vars as these are always kept.

adlb <- tribble(
  ~USUBJID, ~AVISIT,  ~AVAL, ~LBSEQ,
  "1",      "WEEK 1",   123,      1,
  "1",      "WEEK 2",   101,      2,
  "2",      "WEEK 1",    99,      1,
  "2",      "WEEK 2",   110,      2,
  "2",      "WEEK 3",    93,      3
)

derive_extreme_records(
  dataset_add = adlb,
  filter_add = !is.na(AVAL),
  by_vars = exprs(USUBJID),
  order = exprs(AVAL),
  mode = "first",
  keep_source_vars = exprs(AVAL),
  set_values_to = exprs(
    AVISIT = "MINIMUM"
  )
)
#> # A tibble: 2 × 3
#>   USUBJID AVISIT   AVAL
#>   <chr>   <chr>   <dbl>
#> 1 1       MINIMUM   101
#> 2 2       MINIMUM    93

Handling duplicates (check_type)

The source records are checked regarding duplicates with respect to by_vars and order. By default, a warning is issued if any duplicates are found.

adlb <- tribble(
  ~USUBJID, ~AVISIT,  ~AVAL,
  "1",      "WEEK 1",   123,
  "1",      "WEEK 2",   123,
  "2",      "WEEK 1",    99,
  "2",      "WEEK 2",   110,
  "2",      "WEEK 3",    93,
)

derive_extreme_records(
  dataset_add = adlb,
  filter_add = !is.na(AVAL),
  by_vars = exprs(USUBJID),
  order = exprs(AVAL),
  mode = "first",
  set_values_to = exprs(
    AVISIT = "MINIMUM"
  )
)
#> # A tibble: 2 × 3
#>   USUBJID AVISIT   AVAL
#>   <chr>   <chr>   <dbl>
#> 1 1       MINIMUM   123
#> 2 2       MINIMUM    93
#> Warning: Dataset contains duplicate records with respect to `USUBJID` and `AVAL`
#> ℹ Run `admiral::get_duplicates_dataset()` to access the duplicate records

For investigating the issue the dataset of the duplicate source records can be obtained by calling get_duplicates_dataset():

get_duplicates_dataset()
#> Duplicate records with respect to `USUBJID` and `AVAL`.
#> # A tibble: 2 × 3
#>   USUBJID  AVAL AVISIT
#> * <chr>   <dbl> <chr> 
#> 1 1         123 WEEK 1
#> 2 1         123 WEEK 2

Common options to solve the issue are:

• Restricting the source records by specifying/updating the filter_add argument.

• Specifying additional variables for order.

• Setting check_type = "none" to ignore any duplicates.

In this example it doesn't matter which of the records with the minimum value is chosen because it doesn't affect the output dataset. Thus the third option is used:

derive_extreme_records(
  dataset_add = adlb,
  filter_add = !is.na(AVAL),
  by_vars = exprs(USUBJID),
  order = exprs(AVAL),
  mode = "first",
  check_type = "none",
  set_values_to = exprs(
    AVISIT = "MINIMUM"
  )
)
#> # A tibble: 2 × 3
#>   USUBJID AVISIT   AVAL
#>   <chr>   <chr>   <dbl>
#> 1 1       MINIMUM   123
#> 2 2       MINIMUM    93

Flagging existence of source records (exist_flag, true_value, false_value)

If the existence of a source record should be flagged, the exist_flag argument can be specified. The specified variable is set to true_value if a source record exists. Otherwise, it is set to false_value.

The dataset_ref argument should be specified as otherwise all new records originate from dataset_add, i.e., exist_flag would be set to true_value for all records.

adsl <- tribble(
  ~USUBJID, ~DTHDT,
  "1",      ymd("2022-05-13"),
  "2",      ymd(""),
  "3",      ymd("")
)

derive_extreme_records(
  dataset_ref = adsl,
  dataset_add = adsl,
  by_vars = exprs(USUBJID),
  filter_add = !is.na(DTHDT),
  exist_flag = AVALC,
  true_value = "Y",
  false_value = "N",
  set_values_to = exprs(
    PARAMCD = "DEATH",
    ADT = DTHDT
  )
)
#> # A tibble: 3 × 5
#>   USUBJID PARAMCD ADT        DTHDT      AVALC
#>   <chr>   <chr>   <date>     <date>     <chr>
#> 1 1       DEATH   2022-05-13 2022-05-13 Y    
#> 2 2       DEATH   NA         NA         N    
#> 3 3       DEATH   NA         NA         N    

Derive DTYPE = "LOV"

For each subject and parameter the last valid assessment (with respect to AVISITN and LBSEQ) should be selected and added as a new record to the input dataset. For the new records set AVISIT = "PBL LAST", AVISITN = 99, and DTYPE = "LOV".

adlb <- tribble(
  ~USUBJID, ~AVISIT,    ~AVISITN, ~PARAMCD, ~AVAL, ~LBSEQ,
  "1",      "BASELINE",        1, "ABC",      120,      1,
  "1",      "WEEK 1",          2, "ABC",      113,      2,
  "1",      "WEEK 1",          2, "ABC",      117,      3,
  "2",      "BASELINE",        1, "ABC",      101,      1,
  "2",      "WEEK 1",          2, "ABC",      101,      2,
  "2",      "WEEK 2",          3, "ABC",       95,      3,
  "1",      "BASELINE",        1, "DEF",       17,      1,
  "1",      "WEEK 1",          2, "DEF",       NA,      2,
  "1",      "WEEK 1",          2, "DEF",       13,      3,
  "2",      "BASELINE",        1, "DEF",        9,      1,
  "2",      "WEEK 1",          2, "DEF",       10,      2,
  "2",      "WEEK 2",          3, "DEF",       12,      3
) 
mutate(STUDYID = "XYZ", .before = USUBJID)

derive_extreme_records(
  adlb,
  dataset_add = adlb,
  filter_add = !is.na(AVAL) & AVISIT != "BASELINE",
  by_vars = exprs(!!!get_admiral_option("subject_keys"), PARAMCD),
  order = exprs(AVISITN, LBSEQ),
  mode = "last",
  set_values_to = exprs(
    AVISIT = "PBL LAST",
    AVISITN = 99,
    DTYPE = "LOV"
  )
)
#> # A tibble: 16 × 8
#>    STUDYID USUBJID AVISIT   AVISITN PARAMCD  AVAL LBSEQ DTYPE
#>    <chr>   <chr>   <chr>      <dbl> <chr>   <dbl> <dbl> <chr>
#>  1 XYZ     1       BASELINE       1 ABC       120     1 <NA> 
#>  2 XYZ     1       WEEK 1         2 ABC       113     2 <NA> 
#>  3 XYZ     1       WEEK 1         2 ABC       117     3 <NA> 
#>  4 XYZ     2       BASELINE       1 ABC       101     1 <NA> 
#>  5 XYZ     2       WEEK 1         2 ABC       101     2 <NA> 
#>  6 XYZ     2       WEEK 2         3 ABC        95     3 <NA> 
#>  7 XYZ     1       BASELINE       1 DEF        17     1 <NA> 
#>  8 XYZ     1       WEEK 1         2 DEF        NA     2 <NA> 
#>  9 XYZ     1       WEEK 1         2 DEF        13     3 <NA> 
#> 10 XYZ     2       BASELINE       1 DEF         9     1 <NA> 
#> 11 XYZ     2       WEEK 1         2 DEF        10     2 <NA> 
#> 12 XYZ     2       WEEK 2         3 DEF        12     3 <NA> 
#> 13 XYZ     1       PBL LAST      99 ABC       117     3 LOV  
#> 14 XYZ     1       PBL LAST      99 DEF        13     3 LOV  
#> 15 XYZ     2       PBL LAST      99 ABC        95     3 LOV  
#> 16 XYZ     2       PBL LAST      99 DEF        12     3 LOV  

Derive DTYPE = "MINIMUM"

For each subject and parameter the record with the minimum analysis value should be selected and added as a new record to the input dataset. If there are multiple records meeting the minimum, the first record with respect to AVISIT and LBSEQ should be selected. For the new records set AVISIT = "PBL MIN", AVISITN = 97, and DTYPE = "MINIMUM".

derive_extreme_records(
  adlb,
  dataset_add = adlb,
  filter_add = !is.na(AVAL) & AVISIT != "BASELINE",
  by_vars = exprs(!!!get_admiral_option("subject_keys"), PARAMCD),
  order = exprs(AVAL, AVISITN, LBSEQ),
  mode = "first",
  set_values_to = exprs(
    AVISIT = "PBL MIN",
    AVISITN = 97,
    DTYPE = "MINIMUM"
  )
)
#> # A tibble: 16 × 8
#>    STUDYID USUBJID AVISIT   AVISITN PARAMCD  AVAL LBSEQ DTYPE  
#>    <chr>   <chr>   <chr>      <dbl> <chr>   <dbl> <dbl> <chr>  
#>  1 XYZ     1       BASELINE       1 ABC       120     1 <NA>   
#>  2 XYZ     1       WEEK 1         2 ABC       113     2 <NA>   
#>  3 XYZ     1       WEEK 1         2 ABC       117     3 <NA>   
#>  4 XYZ     2       BASELINE       1 ABC       101     1 <NA>   
#>  5 XYZ     2       WEEK 1         2 ABC       101     2 <NA>   
#>  6 XYZ     2       WEEK 2         3 ABC        95     3 <NA>   
#>  7 XYZ     1       BASELINE       1 DEF        17     1 <NA>   
#>  8 XYZ     1       WEEK 1         2 DEF        NA     2 <NA>   
#>  9 XYZ     1       WEEK 1         2 DEF        13     3 <NA>   
#> 10 XYZ     2       BASELINE       1 DEF         9     1 <NA>   
#> 11 XYZ     2       WEEK 1         2 DEF        10     2 <NA>   
#> 12 XYZ     2       WEEK 2         3 DEF        12     3 <NA>   
#> 13 XYZ     1       PBL MIN       97 ABC       113     2 MINIMUM
#> 14 XYZ     1       PBL MIN       97 DEF        13     3 MINIMUM
#> 15 XYZ     2       PBL MIN       97 ABC        95     3 MINIMUM
#> 16 XYZ     2       PBL MIN       97 DEF        10     2 MINIMUM

Derive DTYPE = "MAXIMUM"

For each subject and parameter the record with the maximum analysis value should be selected and added as a new record to the input dataset. If there are multiple records meeting the maximum, the first record with respect to AVISIT and LBSEQ should be selected. For the new records set AVISIT = "PBL MAX", AVISITN = 98, and DTYPE = "MAXIMUM".

derive_extreme_records(
  adlb,
  dataset_add = adlb,
  filter_add = !is.na(AVAL) & AVISIT != "BASELINE",
  by_vars = exprs(!!!get_admiral_option("subject_keys"), PARAMCD),
  order = exprs(desc(AVAL), AVISITN, LBSEQ),
  mode = "first",
  set_values_to = exprs(
    AVISIT = "PBL MAX",
    AVISITN = 99,
    DTYPE = "MAXIMUM"
  )
)
#> # A tibble: 16 × 8
#>    STUDYID USUBJID AVISIT   AVISITN PARAMCD  AVAL LBSEQ DTYPE  
#>    <chr>   <chr>   <chr>      <dbl> <chr>   <dbl> <dbl> <chr>  
#>  1 XYZ     1       BASELINE       1 ABC       120     1 <NA>   
#>  2 XYZ     1       WEEK 1         2 ABC       113     2 <NA>   
#>  3 XYZ     1       WEEK 1         2 ABC       117     3 <NA>   
#>  4 XYZ     2       BASELINE       1 ABC       101     1 <NA>   
#>  5 XYZ     2       WEEK 1         2 ABC       101     2 <NA>   
#>  6 XYZ     2       WEEK 2         3 ABC        95     3 <NA>   
#>  7 XYZ     1       BASELINE       1 DEF        17     1 <NA>   
#>  8 XYZ     1       WEEK 1         2 DEF        NA     2 <NA>   
#>  9 XYZ     1       WEEK 1         2 DEF        13     3 <NA>   
#> 10 XYZ     2       BASELINE       1 DEF         9     1 <NA>   
#> 11 XYZ     2       WEEK 1         2 DEF        10     2 <NA>   
#> 12 XYZ     2       WEEK 2         3 DEF        12     3 <NA>   
#> 13 XYZ     1       PBL MAX       99 ABC       117     3 MAXIMUM
#> 14 XYZ     1       PBL MAX       99 DEF        13     3 MAXIMUM
#> 15 XYZ     2       PBL MAX       99 ABC       101     2 MAXIMUM
#> 16 XYZ     2       PBL MAX       99 DEF        12     3 MAXIMUM

Derive DTYPE = "WOC" or DTYPE = "BOC"

For each subject and parameter the record with the worst analysis value should be selected and added as a new record to the input dataset. The worst value is either the minimum or maximum value depending on the parameter. If there are multiple records meeting the worst value, the first record with respect to AVISIT and LBSEQ should be selected. For the new records set AVISIT = "PBL WORST", AVISITN = 96, and DTYPE = "WOC".

Here the maximum is considered worst for PARAMCD = "ABC" and the minimum for PARAMCD = "DEF".

derive_extreme_records(
  adlb,
  dataset_add = adlb,
  filter_add = !is.na(AVAL) & AVISIT != "BASELINE",
  by_vars = exprs(!!!get_admiral_option("subject_keys"), PARAMCD),
  order = exprs(
    if_else(PARAMCD == "ABC", desc(AVAL), AVAL),
    AVISITN, LBSEQ
  ),
  mode = "first",
  set_values_to = exprs(
    AVISIT = "PBL WORST",
    AVISITN = 96,
    DTYPE = "WOC"
  )
)
#> # A tibble: 16 × 8
#>    STUDYID USUBJID AVISIT    AVISITN PARAMCD  AVAL LBSEQ DTYPE
#>    <chr>   <chr>   <chr>       <dbl> <chr>   <dbl> <dbl> <chr>
#>  1 XYZ     1       BASELINE        1 ABC       120     1 <NA> 
#>  2 XYZ     1       WEEK 1          2 ABC       113     2 <NA> 
#>  3 XYZ     1       WEEK 1          2 ABC       117     3 <NA> 
#>  4 XYZ     2       BASELINE        1 ABC       101     1 <NA> 
#>  5 XYZ     2       WEEK 1          2 ABC       101     2 <NA> 
#>  6 XYZ     2       WEEK 2          3 ABC        95     3 <NA> 
#>  7 XYZ     1       BASELINE        1 DEF        17     1 <NA> 
#>  8 XYZ     1       WEEK 1          2 DEF        NA     2 <NA> 
#>  9 XYZ     1       WEEK 1          2 DEF        13     3 <NA> 
#> 10 XYZ     2       BASELINE        1 DEF         9     1 <NA> 
#> 11 XYZ     2       WEEK 1          2 DEF        10     2 <NA> 
#> 12 XYZ     2       WEEK 2          3 DEF        12     3 <NA> 
#> 13 XYZ     1       PBL WORST      96 ABC       117     3 WOC  
#> 14 XYZ     1       PBL WORST      96 DEF        13     3 WOC  
#> 15 XYZ     2       PBL WORST      96 ABC       101     2 WOC  
#> 16 XYZ     2       PBL WORST      96 DEF        10     2 WOC  

Derive a parameter for the first disease progression (PD)

For each subject in the ADSL dataset a new parameter should be added to the input dataset which indicates whether disease progression (PD) occurred (set AVALC = "Y", AVAL = 1) or not (set AVALC = "N", AVAL = 0). For the new parameter set PARAMCD = "PD" and PARAM = "Disease Progression".

adsl <- tribble(
  ~USUBJID, ~DTHDT,
  "1",      ymd("2022-05-13"),
  "2",      ymd(""),
  "3",      ymd("")
) 
  mutate(STUDYID = "XX1234")

adrs <- tribble(
  ~USUBJID, ~RSDTC,       ~AVALC, ~AVAL,
  "1",      "2020-01-02", "PR",       2,
  "1",      "2020-02-01", "CR",       1,
  "1",      "2020-03-01", "CR",       1,
  "2",      "2021-06-15", "SD",       3,
  "2",      "2021-07-16", "PD",       4,
  "2",      "2021-09-14", "PD",       4
) 
  mutate(
    STUDYID = "XX1234", .before = USUBJID
  ) 
  mutate(
    ADT = ymd(RSDTC),
    PARAMCD = "OVR",
    PARAM = "Overall Response",
    .after = RSDTC
  )

derive_extreme_records(
  adrs,
  dataset_ref = adsl,
  dataset_add = adrs,
  by_vars = get_admiral_option("subject_keys"),
  filter_add = PARAMCD == "OVR" & AVALC == "PD",
  order = exprs(ADT),
  exist_flag = AVALC,
  true_value = "Y",
  false_value = "N",
  mode = "first",
  set_values_to = exprs(
    PARAMCD = "PD",
    PARAM = "Disease Progression",
    AVAL = yn_to_numeric(AVALC),
  )
)
#> # A tibble: 9 × 8
#>   STUDYID USUBJID RSDTC      ADT        PARAMCD PARAM               AVALC  AVAL
#>   <chr>   <chr>   <chr>      <date>     <chr>   <chr>               <chr> <dbl>
#> 1 XX1234  1       2020-01-02 2020-01-02 OVR     Overall Response    PR        2
#> 2 XX1234  1       2020-02-01 2020-02-01 OVR     Overall Response    CR        1
#> 3 XX1234  1       2020-03-01 2020-03-01 OVR     Overall Response    CR        1
#> 4 XX1234  2       2021-06-15 2021-06-15 OVR     Overall Response    SD        3
#> 5 XX1234  2       2021-07-16 2021-07-16 OVR     Overall Response    PD        4
#> 6 XX1234  2       2021-09-14 2021-09-14 OVR     Overall Response    PD        4
#> 7 XX1234  2       2021-07-16 2021-07-16 PD      Disease Progression Y         1
#> 8 XX1234  1       <NA>       NA         PD      Disease Progression N         0
#> 9 XX1234  3       <NA>       NA         PD      Disease Progression N         0

Derive parameter indicating death

For each subject in the ADSL dataset a new parameter should be created which indicates whether the subject died (set AVALC = "Y", AVAL = 1) or not (set AVALC = "N", AVAL = 0). For the new parameter set PARAMCD = "DEATH", PARAM = "Death", and ADT to the date of death (DTHDT).

derive_extreme_records(
  dataset_ref = adsl,
  dataset_add = adsl,
  by_vars = exprs(STUDYID, USUBJID),
  filter_add = !is.na(DTHDT),
  exist_flag = AVALC,
  true_value = "Y",
  false_value = "N",
  mode = "first",
  keep_source_vars = exprs(AVALC),
  set_values_to = exprs(
    PARAMCD = "DEATH",
    PARAM = "Death",
    ADT = DTHDT
  )
)
#> # A tibble: 3 × 6
#>   STUDYID USUBJID PARAMCD PARAM ADT        AVALC
#>   <chr>   <chr>   <chr>   <chr> <date>     <chr>
#> 1 XX1234  1       DEATH   Death 2022-05-13 Y    
#> 2 XX1234  2       DEATH   Death NA         N    
#> 3 XX1234  3       DEATH   Death NA         N    

The keep_source_vars argument is specified to avoid that all ADLS variables (like DTHDT) are copied to the parameter.

See Also

derive_summary_records()

BDS-Findings Functions for adding Parameters/Records: default_qtc_paramcd(), derive_expected_records(), derive_extreme_event(), derive_locf_records(), derive_param_bmi(), derive_param_bsa(), derive_param_computed(), derive_param_doseint(), derive_param_exist_flag(), derive_param_exposure(), derive_param_framingham(), derive_param_map(), derive_param_qtc(), derive_param_rr(), derive_param_wbc_abs(), derive_summary_records()

Roche-GSK/admiral documentation built on April 14, 2025, 12:36 p.m.