derive_vars_joined: Add Variables from an Additional Dataset Based on Conditions...

In admiral: ADaM in R Asset Library

Add Variables from an Additional Dataset Based on Conditions from Both Datasets

Description

The function adds variables from an additional dataset to the input dataset. The selection of the observations from the additional dataset can depend on variables from both datasets. For example, add the lowest value (nadir) before the current observation.

Usage

derive_vars_joined(
  dataset,
  dataset_add,
  by_vars = NULL,
  order = NULL,
  new_vars = NULL,
  tmp_obs_nr_var = NULL,
  join_vars = NULL,
  join_type,
  filter_add = NULL,
  first_cond_lower = NULL,
  first_cond_upper = NULL,
  filter_join = NULL,
  mode = NULL,
  exist_flag = NULL,
  true_value = "Y",
  false_value = NA_character_,
  missing_values = NULL,
  check_type = "warning"
)

Arguments

dataset	Input dataset The variables specified by the by_vars argument are expected to be in the dataset. Permitted values a dataset, i.e., a data.frame or tibble Default value none
dataset_add	Additional dataset The variables specified by the by_vars, the new_vars, the join_vars, and the order argument are expected. Permitted values a dataset, i.e., a data.frame or tibble Default value none
by_vars	Grouping variables The two datasets are joined by the specified variables. Variables can be renamed by naming the element, i.e. ⁠by_vars = exprs(<name in input dataset> = <name in additional dataset>)⁠, similar to the dplyr joins. Permitted values list of variables created by exprs(), e.g., exprs(USUBJID, VISIT) Default value NULL
order	Sort order If the argument is set to a non-null value, for each observation of the input dataset the first or last observation from the joined dataset is selected with respect to the specified order. The specified variables are expected in the additional dataset (dataset_add). If a variable is available in both dataset and dataset_add, the one from dataset_add is used for the sorting. If an expression is named, e.g., exprs(EXSTDT = convert_dtc_to_dt(EXSTDTC), EXSEQ), a corresponding variable (EXSTDT) is added to the additional dataset and can be used in the filter conditions (filter_add, filter_join) and for join_vars and new_vars. The variable is not included in the output dataset. For handling of NAs in sorting variables see Sort Order. Permitted values list of variables created by exprs(), e.g., exprs(USUBJID, VISIT) Default value NULL
new_vars	Variables to add The specified variables from the additional dataset are added to the output dataset. Variables can be renamed by naming the element, i.e., ⁠new_vars = exprs(<new name> = <old name>)⁠. For example new_vars = exprs(var1, var2) adds variables var1 and var2 from dataset_add to the input dataset. And new_vars = exprs(var1, new_var2 = old_var2) takes var1 and old_var2 from dataset_add and adds them to the input dataset renaming old_var2 to new_var2. Values of the added variables can be modified by specifying an expression. For example, new_vars = LASTRSP = exprs(str_to_upper(AVALC)) adds the variable LASTRSP to the dataset and sets it to the upper case value of AVALC. If the argument is not specified or set to NULL, all variables from the additional dataset (dataset_add) are added. In the case when a variable exists in both datasets, an error is issued to ensure the user either adds to by_vars, removes or renames. Permitted values list of variables created by exprs(), e.g., exprs(USUBJID, VISIT) Default value NULL
tmp_obs_nr_var	Temporary observation number The specified variable is added to the input dataset (dataset) and the additional dataset (dataset_add). It is set to the observation number with respect to order. For each by group (by_vars) the observation number starts with 1. If there is more than one record for specific values for by_vars and order, all records get the same observation number. By default, a warning (see check_type) is issued in this case. The variable can be used in the conditions (filter_join, first_cond_upper, first_cond_lower). It can also be used to select consecutive observations or the last observation. The variable is not included in the output dataset. To include it specify it for new_vars. Permitted values an unquoted symbol, e.g., AVAL Default value NULL
join_vars	Variables to use from additional dataset Any extra variables required from the additional dataset for filter_join should be specified for this argument. Variables specified for new_vars do not need to be repeated for join_vars. If a specified variable exists in both the input dataset and the additional dataset, the suffix ".join" is added to the variable from the additional dataset. If an expression is named, e.g., exprs(EXTDT = convert_dtc_to_dt(EXSTDTC)), a corresponding variable is added to the additional dataset and can be used in the filter conditions (filter_add, filter_join) and for new_vars. The variable is not included in the output dataset. The variables are not included in the output dataset. Permitted values list of variables created by exprs(), e.g., exprs(USUBJID, VISIT) Default value NULL
join_type	Observations to keep after joining The argument determines which of the joined observations are kept with respect to the original observation. For example, if join_type = "after" is specified all observations after the original observations are kept. For example for confirmed response or BOR in the oncology setting or confirmed deterioration in questionnaires the confirmatory assessment must be after the assessment. Thus join_type = "after" could be used. Whereas, sometimes you might allow for confirmatory observations to occur prior to the observation. For example, to identify AEs occurring on or after seven days before a COVID AE. Thus join_type = "all" could be used. Permitted values "before", "after", "all" Default value none
filter_add	Filter for additional dataset (dataset_add) Only observations from dataset_add fulfilling the specified condition are joined to the input dataset. If the argument is not specified, all observations are joined. Variables created by order or new_vars arguments can be used in the condition. The condition can include summary functions like all() or any(). The additional dataset is grouped by the by variables (by_vars). Permitted values an unquoted condition, e.g., AVISIT == "BASELINE" Default value NULL
first_cond_lower	Condition for selecting range of data (before) If this argument is specified, the other observations are restricted from the last observation before the current observation where the specified condition is fulfilled up to the current observation. If the condition is not fulfilled for any of the other observations, no observations are considered. This argument should be specified if filter_join contains summary functions which should not apply to all observations but only from a certain observation before the current observation up to the current observation. For an example, see the "Examples" section below. Permitted values an unquoted condition, e.g., AVISIT == "BASELINE" Default value NULL
first_cond_upper	Condition for selecting range of data (after) If this argument is specified, the other observations are restricted up to the first observation where the specified condition is fulfilled. If the condition is not fulfilled for any of the other observations, no observations are considered. This argument should be specified if filter_join contains summary functions which should not apply to all observations but only up to the confirmation assessment. For an example, see the "Examples" section below. Permitted values an unquoted condition, e.g., AVISIT == "BASELINE" Default value NULL
filter_join	Filter for the joined dataset The specified condition is applied to the joined dataset. Therefore variables from both datasets dataset and dataset_add can be used. Variables created by order or new_vars arguments can be used in the condition. The condition can include summary functions like all() or any(). The joined dataset is grouped by the original observations. Permitted values an unquoted condition, e.g., AVISIT == "BASELINE" Default value NULL
mode	Selection mode Determines if the first or last observation is selected. If the order argument is specified, mode must be non-null. If the order argument is not specified, the mode argument is ignored. Permitted values "first", "last" Default value NULL
exist_flag	Exist flag If the argument is specified (e.g., exist_flag = FLAG), the specified variable (e.g., FLAG) is added to the input dataset. This variable will be the value provided in true_value for all selected records from dataset_add which are merged into the input dataset, and the value provided in false_value otherwise. Permitted values an unquoted symbol, e.g., AVAL Default value NULL
true_value	True value The value for the specified variable exist_flag, applicable to the first or last observation (depending on the mode) of each by group. Permitted values a character scalar, i.e., a character vector of length one Default value "Y"
false_value	False value The value for the specified variable exist_flag, NOT applicable to the first or last observation (depending on the mode) of each by group. Permitted values a character scalar, i.e., a character vector of length one Default value NA_character_
missing_values	Values for non-matching observations For observations of the input dataset (dataset) which do not have a matching observation in the additional dataset (dataset_add) the values of the specified variables are set to the specified value. Only variables specified for new_vars can be specified for missing_values. Permitted values list of named expressions created by a formula using exprs(), e.g., exprs(AVALC = VSSTRESC, AVAL = yn_to_numeric(AVALC)) Default value NULL
check_type	Check uniqueness? If "message", "warning" or "error" is specified, the specified message is issued if the observations of the (restricted) joined dataset are not unique with respect to the by variables and the order. This argument is ignored if order is not specified. In this case an error is issued independent of check_type if the restricted joined dataset contains more than one observation for any of the observations of the input dataset. Permitted values "none", "message", "warning", "error" Default value "warning"

Details

1. The variables specified by order are added to the additional dataset (dataset_add).

2. The variables specified by join_vars are added to the additional dataset (dataset_add).

3. The records from the additional dataset (dataset_add) are restricted to those matching the filter_add condition.

4. The input dataset and the (restricted) additional dataset are left joined by the grouping variables (by_vars). If no grouping variables are specified, a full join is performed.

5. If first_cond_lower is specified, for each observation of the input dataset the joined dataset is restricted to observations from the first observation where first_cond_lower is fulfilled (the observation fulfilling the condition is included) up to the observation of the input dataset. If for an observation of the input dataset the condition is not fulfilled, the observation is removed.

   If first_cond_upper is specified, for each observation of the input dataset the joined dataset is restricted to observations up to the first observation where first_cond_upper is fulfilled (the observation fulfilling the condition is included). If for an observation of the input dataset the condition is not fulfilled, the observation is removed.

   For an example, see the "Examples" section below.

6. The joined dataset is restricted by the filter_join condition.

7. If order is specified, for each observation of the input dataset the first or last observation (depending on mode) is selected.

8. The variables specified for new_vars are created (if requested) and merged to the input dataset. I.e., the output dataset contains all observations from the input dataset. For observations without a matching observation in the joined dataset the new variables are set as specified by missing_values (or to NA for variables not in missing_values). Observations in the additional dataset which have no matching observation in the input dataset are ignored.

Note: This function creates temporary datasets which may be much bigger than the input datasets. If this causes memory issues, please try setting the admiral option save_memory to TRUE (see set_admiral_options()). This reduces the memory consumption but increases the run-time.

Value

The output dataset contains all observations and variables of the input dataset and additionally the variables specified for new_vars from the additional dataset (dataset_add).

Examples

Note on usage versus derive_vars_merged()

The question between using derive_vars_merged() or the more powerful derive_vars_joined() comes down to how you need to select the observations to be merged.

• If the observations from dataset_add to merge can be selected by a condition (filter_add) using only variables from dataset_add, then always use derive_vars_merged() as it requires less resources (time and memory). A common example of this would be a randomization date in ADSL, where you are simply merging on a date from DS according to a certain DSDECOD condition such as DSDECOD == "RANDOMIZATION".

• However, if the selection of the observations from dataset_add can depend on variables from both datasets, then use derive_vars_joined(). An example of this would be assigning period variables from ADSL to an ADAE, where you now need to check each adverse event start date against the period start and end dates to decide which period value to join.

Basic join based on a generic time window (filter_join)

Derive a visit based on where the study day falls according to a scheduled set of time windows.

• The filter_join argument here can check conditions using variables from both the dataset and dataset_add, so the study day is compared to the start and end of the time window.

• As no grouping variables are assigned using the by_vars argument, a full join is performed keeping all variables from dataset_add.

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

adbds <- tribble(
  ~USUBJID, ~ADY, ~AVAL,
  "1",       -33,    11,
  "1",        -7,    10,
  "1",         1,    12,
  "1",         8,    12,
  "1",        15,     9,
  "1",        20,    14,
  "1",        24,    12,
  "2",        -1,    13,
  "2",        13,     8
) %>%
  mutate(STUDYID = "AB42")

windows <- tribble(
  ~AVISIT,    ~AWLO, ~AWHI,
  "BASELINE",   -30,     1,
  "WEEK 1",       2,     7,
  "WEEK 2",       8,    15,
  "WEEK 3",      16,    22,
  "WEEK 4",      23,    30
)

derive_vars_joined(
  adbds,
  dataset_add = windows,
  join_type = "all",
  filter_join = AWLO <= ADY & ADY <= AWHI
) %>%
  select(USUBJID, ADY, AWLO, AWHI, AVISIT)
#> # A tibble: 9 × 5
#>   USUBJID   ADY  AWLO  AWHI AVISIT  
#>   <chr>   <dbl> <dbl> <dbl> <chr>   
#> 1 1         -33    NA    NA <NA>    
#> 2 1          -7   -30     1 BASELINE
#> 3 1           1   -30     1 BASELINE
#> 4 1           8     8    15 WEEK 2  
#> 5 1          15     8    15 WEEK 2  
#> 6 1          20    16    22 WEEK 3  
#> 7 1          24    23    30 WEEK 4  
#> 8 2          -1   -30     1 BASELINE
#> 9 2          13     8    15 WEEK 2  

Join only the lowest/highest value occurring within a condition (filter_join, order and mode)

Derive the nadir value for each observation (i.e. the lowest value occurring before) by subject.

• Note how dataset and dataset_add are the same here, so we are joining a dataset with itself. This enables us to compare records within the dataset to each other.

• Now we use by_vars as we only want to perform the join by subject.

• To find the lowest value we use the order and mode arguments.

• We subsequently need to check ADY to only check assessments occurring before. As this is not included in by_vars or order, we have to ensure it also gets joined by adding to join_vars. Then in filter_join note how ADY.join < ADY is used as the same variable exists in both datasets, so the version from dataset_add has .join added.

• According to the AVAL sort order used there could be duplicates (e.g. see subject "1" records at day 1 and 8), but given we only need to join AVAL itself here it doesn't actually matter to us which exact record is taken. So, in this example, we silence the uniqueness check by using check_type = "none".

derive_vars_joined(
  adbds,
  dataset_add = adbds,
  by_vars = exprs(STUDYID, USUBJID),
  order = exprs(AVAL),
  new_vars = exprs(NADIR = AVAL),
  join_vars = exprs(ADY),
  join_type = "all",
  filter_join = ADY.join < ADY,
  mode = "first",
  check_type = "none"
) %>%
  select(USUBJID, ADY, AVAL, NADIR)
#> # A tibble: 9 × 4
#>   USUBJID   ADY  AVAL NADIR
#>   <chr>   <dbl> <dbl> <dbl>
#> 1 1         -33    11    NA
#> 2 1          -7    10    11
#> 3 1           1    12    10
#> 4 1           8    12    10
#> 5 1          15     9    10
#> 6 1          20    14     9
#> 7 1          24    12     9
#> 8 2          -1    13    NA
#> 9 2          13     8    13

Filtering which records are joined from the additional dataset (filter_add)

Imagine we wanted to achieve the same as above, but we now want to derive this allowing only post-baseline values to be possible for the nadir.

• The filter_add argument can be used here as we only need to restrict the source data from dataset_add.

derive_vars_joined(
  adbds,
  dataset_add = adbds,
  by_vars = exprs(STUDYID, USUBJID),
  order = exprs(AVAL),
  new_vars = exprs(NADIR = AVAL),
  join_vars = exprs(ADY),
  join_type = "all",
  filter_add = ADY > 0,
  filter_join = ADY.join < ADY,
  mode = "first",
  check_type = "none"
) %>%
  select(USUBJID, ADY, AVAL, NADIR)
#> # A tibble: 9 × 4
#>   USUBJID   ADY  AVAL NADIR
#>   <chr>   <dbl> <dbl> <dbl>
#> 1 1         -33    11    NA
#> 2 1          -7    10    NA
#> 3 1           1    12    NA
#> 4 1           8    12    12
#> 5 1          15     9    12
#> 6 1          20    14     9
#> 7 1          24    12     9
#> 8 2          -1    13    NA
#> 9 2          13     8    NA

Combining all of the above examples

Using all of the arguments demonstrated above, here is a more complex example to add to ADAE the highest hemoglobin value occurring within two weeks before each adverse event. Also join the day it occurred, taking the earliest occurrence if more than one assessment with the same value.

• Note how we used mode = "last" to get the highest lab value, but then as we wanted the earliest occurrence if more than one it means we need to add desc(ADY) to order. i.e. the last day when in descending order is the first.

adae <- tribble(
  ~USUBJID, ~ASTDY,
  "1",           3,
  "1",          22,
  "2",           2
) %>%
  mutate(STUDYID = "AB42")

adlb <- tribble(
  ~USUBJID, ~PARAMCD, ~ADY, ~AVAL,
  "1",      "HGB",       1,   8.5,
  "1",      "HGB",       3,   7.9,
  "1",      "HGB",       5,   8.9,
  "1",      "HGB",       8,   8.0,
  "1",      "HGB",       9,   8.0,
  "1",      "HGB",      16,   7.4,
  "1",      "ALB",       1,    42,
) %>%
  mutate(STUDYID = "AB42")

derive_vars_joined(
  adae,
  dataset_add = adlb,
  by_vars = exprs(STUDYID, USUBJID),
  order = exprs(AVAL, desc(ADY)),
  new_vars = exprs(HGB_MAX = AVAL, HGB_DY = ADY),
  join_type = "all",
  filter_add = PARAMCD == "HGB",
  filter_join = ASTDY - 14 <= ADY & ADY <= ASTDY,
  mode = "last"
) %>%
  select(USUBJID, ASTDY, HGB_MAX, HGB_DY)
#> # A tibble: 3 × 4
#>   USUBJID ASTDY HGB_MAX HGB_DY
#>   <chr>   <dbl>   <dbl>  <dbl>
#> 1 1           3     8.5      1
#> 2 1          22     8        8
#> 3 2           2    NA       NA

Compute values in new_vars and order

Add to ADAE the number of days since the last dose of treatment, plus 1 day. If the dose occurs on the same day as the AE then include it as the last dose.

• In the new_vars argument, other functions can be utilized to modify the joined values using variables from both dataset and dataset_add. For example, in the below case we want to calculate the number of days between the AE and the last dose using compute_duration(). This function includes the plus 1 day as default.

• Also note how in this example EXSDT is created via the order argument and then used for new_vars, filter_add and filter_join.

• The reason to use join_type = "all" here instead of "before" is that we want to include any dose occurring on the same day as the AE, hence the filter_join = EXSDT <= ASTDT. Whereas using join_type = "before" would have resulted in the condition EXSDT < ASTDT. See the next example instead for join_type = "before".

adae <- tribble(
  ~USUBJID, ~ASTDT,
  "1",      "2020-02-02",
  "1",      "2020-02-04",
  "2",      "2021-01-08"
) %>%
  mutate(
    ASTDT = ymd(ASTDT),
    STUDYID = "AB42"
  )

ex <- tribble(
  ~USUBJID, ~EXSDTC,
  "1",      "2020-01-10",
  "1",      "2020-01",
  "1",      "2020-01-20",
  "1",      "2020-02-03",
  "2",      "2021-01-05"
) %>%
  mutate(STUDYID = "AB42")

derive_vars_joined(
  adae,
  dataset_add = ex,
  by_vars = exprs(STUDYID, USUBJID),
  order = exprs(EXSDT = convert_dtc_to_dt(EXSDTC)),
  join_type = "all",
  new_vars = exprs(LDRELD = compute_duration(
    start_date = EXSDT, end_date = ASTDT
  )),
  filter_add = !is.na(EXSDT),
  filter_join = EXSDT <= ASTDT,
  mode = "last"
) %>%
  select(USUBJID, ASTDT, LDRELD)
#> # A tibble: 3 × 3
#>   USUBJID ASTDT      LDRELD
#>   <chr>   <date>      <dbl>
#> 1 1       2020-02-02     14
#> 2 1       2020-02-04      2
#> 3 2       2021-01-08      4

Join records occurring before a condition (join_type = "before")

In an arbitrary dataset where subjects have values of "0", "-", "+" or "++", for any value of "0" derive the last occurring "++" day that occurs before the "0".

• The AVAL.join == "++" in filter_join, along with order and mode taking the last day, identifies the target records to join from dataset_add for each observation of dataset.

• Then join_type = "before" is now used instead of join_type = "all". This is because we only want to join the records occurring before the current observation in dataset. Including AVAL == "0" in filter_join ensures here that we only populate the new variable for records with AVAL == "0" in our dataset.

myd <- tribble(
  ~USUBJID, ~ADY, ~AVAL,
  "1",         1, "++",
  "1",         2, "-",
  "1",         3, "0",
  "1",         4, "+",
  "1",         5, "++",
  "1",         6, "-",
  "2",         1, "-",
  "2",         2, "++",
  "2",         3, "+",
  "2",         4, "0",
  "2",         5, "-",
  "2",         6, "++",
  "2",         7, "0"
) %>%
  mutate(STUDYID = "AB42")

derive_vars_joined(
  myd,
  dataset_add = myd,
  by_vars = exprs(STUDYID, USUBJID),
  order = exprs(ADY),
  mode = "last",
  new_vars = exprs(PREVPLDY = ADY),
  join_vars = exprs(AVAL),
  join_type = "before",
  filter_join = AVAL == "0" & AVAL.join == "++"
) %>%
  select(USUBJID, ADY, AVAL, PREVPLDY)
#> # A tibble: 13 × 4
#>    USUBJID   ADY AVAL  PREVPLDY
#>    <chr>   <dbl> <chr>    <dbl>
#>  1 1           1 ++          NA
#>  2 1           2 -           NA
#>  3 1           3 0            1
#>  4 1           4 +           NA
#>  5 1           5 ++          NA
#>  6 1           6 -           NA
#>  7 2           1 -           NA
#>  8 2           2 ++          NA
#>  9 2           3 +           NA
#> 10 2           4 0            2
#> 11 2           5 -           NA
#> 12 2           6 ++          NA
#> 13 2           7 0            6

Join records occurring before a condition and checking all values in between (first_cond_lower, join_type and filter_join)

In the same example as above, now additionally check that in between the "++" and the "0" all results must be either "+" or "++".

• Firstly, first_cond_lower = AVAL.join == "++" is used so that for each observation of dataset the joined records from dataset_add are restricted to only include from the last occurring "++" before. This is necessary because of the use of a summary function in filter_join only on a subset of the joined observations as explained below.

• The filter_join condition used here now includes all(AVAL.join %in% c("+", "++")) to further restrict the joined records from dataset_add to only where all the values are either "+" or "++".

• The order and mode arguments ensure only the day of the "++" value is joined. For example, for subject "2" it selects the day 2 record instead of day 3, by using "first".

derive_vars_joined(
  myd,
  dataset_add = myd,
  by_vars = exprs(STUDYID, USUBJID),
  order = exprs(ADY),
  mode = "first",
  new_vars = exprs(PREVPLDY = ADY),
  join_vars = exprs(AVAL),
  join_type = "before",
  first_cond_lower = AVAL.join == "++",
  filter_join = AVAL == "0" & all(AVAL.join %in% c("+", "++"))
) %>%
  select(USUBJID, ADY, AVAL, PREVPLDY)
#> # A tibble: 13 × 4
#>    USUBJID   ADY AVAL  PREVPLDY
#>    <chr>   <dbl> <chr>    <dbl>
#>  1 1           1 ++          NA
#>  2 1           2 -           NA
#>  3 1           3 0           NA
#>  4 1           4 +           NA
#>  5 1           5 ++          NA
#>  6 1           6 -           NA
#>  7 2           1 -           NA
#>  8 2           2 ++          NA
#>  9 2           3 +           NA
#> 10 2           4 0            2
#> 11 2           5 -           NA
#> 12 2           6 ++          NA
#> 13 2           7 0            6

Join records occurring after a condition checking all values in between (first_cond_upper, join_type and filter_join)

Similar to the above, now derive the first "++" day after any "0" where all results in between are either "+" or "++".

• Note how the main difference here is the use of join_type = "after", mode = "last" and the first_cond_upper argument, instead of first_cond_lower.

derive_vars_joined(
  myd,
  dataset_add = myd,
  by_vars = exprs(STUDYID, USUBJID),
  order = exprs(ADY),
  mode = "last",
  new_vars = exprs(NEXTPLDY = ADY),
  join_vars = exprs(AVAL),
  join_type = "after",
  first_cond_upper = AVAL.join == "++",
  filter_join = AVAL == "0" & all(AVAL.join %in% c("+", "++"))
) %>%
  select(USUBJID, ADY, AVAL, NEXTPLDY)
#> # A tibble: 13 × 4
#>    USUBJID   ADY AVAL  NEXTPLDY
#>    <chr>   <dbl> <chr>    <dbl>
#>  1 1           1 ++          NA
#>  2 1           2 -           NA
#>  3 1           3 0            5
#>  4 1           4 +           NA
#>  5 1           5 ++          NA
#>  6 1           6 -           NA
#>  7 2           1 -           NA
#>  8 2           2 ++          NA
#>  9 2           3 +           NA
#> 10 2           4 0           NA
#> 11 2           5 -           NA
#> 12 2           6 ++          NA
#> 13 2           7 0           NA

Join a value from the next occurring record (join_type = "after")

Add the value from the next occurring record as a new variable.

• The join_type = "after" here essentially acts as a lag to join variables from the next occurring record, and mode = "first" selects the first of these.

derive_vars_joined(
  myd,
  dataset_add = myd,
  by_vars = exprs(STUDYID, USUBJID),
  order = exprs(ADY),
  mode = "first",
  new_vars = exprs(NEXTVAL = AVAL),
  join_vars = exprs(AVAL),
  join_type = "after"
) %>%
  select(USUBJID, ADY, AVAL, NEXTVAL)
#> # A tibble: 13 × 4
#>    USUBJID   ADY AVAL  NEXTVAL
#>    <chr>   <dbl> <chr> <chr>  
#>  1 1           1 ++    -      
#>  2 1           2 -     0      
#>  3 1           3 0     +      
#>  4 1           4 +     ++     
#>  5 1           5 ++    -      
#>  6 1           6 -     <NA>   
#>  7 2           1 -     ++     
#>  8 2           2 ++    +      
#>  9 2           3 +     0      
#> 10 2           4 0     -      
#> 11 2           5 -     ++     
#> 12 2           6 ++    0      
#> 13 2           7 0     <NA>   

Join records after a condition occurring in consecutive visits (tmp_obs_nr_var, join_type and filter_join)

Find the last occurring value on any of the next 3 unique visit days.

• The tmp_obs_nr_var argument can be useful as shown here to help pick out records happening before or after with respect to order, as you can see in the filter_join.

derive_vars_joined(
  myd,
  dataset_add = myd,
  by_vars = exprs(STUDYID, USUBJID),
  order = exprs(ADY),
  mode = "last",
  new_vars = exprs(NEXTVAL = AVAL),
  tmp_obs_nr_var = tmp_obs_nr,
  join_vars = exprs(AVAL),
  join_type = "after",
  filter_join = tmp_obs_nr + 3 >= tmp_obs_nr.join
) %>%
  select(USUBJID, ADY, AVAL, NEXTVAL)
#> # A tibble: 13 × 4
#>    USUBJID   ADY AVAL  NEXTVAL
#>    <chr>   <dbl> <chr> <chr>  
#>  1 1           1 ++    +      
#>  2 1           2 -     ++     
#>  3 1           3 0     -      
#>  4 1           4 +     -      
#>  5 1           5 ++    -      
#>  6 1           6 -     <NA>   
#>  7 2           1 -     0      
#>  8 2           2 ++    -      
#>  9 2           3 +     ++     
#> 10 2           4 0     0      
#> 11 2           5 -     0      
#> 12 2           6 ++    0      
#> 13 2           7 0     <NA>   

Derive period variables (APERIOD, APERSDT, APEREDT)

Create a period reference dataset from ADSL and join this with ADAE to identify within which period each AE occurred.

adsl <- tribble(
  ~USUBJID, ~AP01SDT,     ~AP01EDT,     ~AP02SDT,     ~AP02EDT,
  "1",      "2021-01-04", "2021-02-06", "2021-02-07", "2021-03-07",
  "2",      "2021-02-02", "2021-03-02", "2021-03-03", "2021-04-01"
) %>%
  mutate(across(ends_with("DT"), ymd)) %>%
  mutate(STUDYID = "AB42")

period_ref <- create_period_dataset(
  adsl,
  new_vars = exprs(APERSDT = APxxSDT, APEREDT = APxxEDT)
)

period_ref
#> # A tibble: 4 × 5
#>   STUDYID USUBJID APERIOD APERSDT    APEREDT   
#>   <chr>   <chr>     <int> <date>     <date>    
#> 1 AB42    1             1 2021-01-04 2021-02-06
#> 2 AB42    1             2 2021-02-07 2021-03-07
#> 3 AB42    2             1 2021-02-02 2021-03-02
#> 4 AB42    2             2 2021-03-03 2021-04-01

adae <- tribble(
  ~USUBJID, ~ASTDT,
  "1",      "2021-01-01",
  "1",      "2021-01-05",
  "1",      "2021-02-05",
  "1",      "2021-03-05",
  "1",      "2021-04-05",
  "2",      "2021-02-15",
) %>%
  mutate(
    ASTDT = ymd(ASTDT),
    STUDYID = "AB42"
  )

derive_vars_joined(
  adae,
  dataset_add = period_ref,
  by_vars = exprs(STUDYID, USUBJID),
  join_vars = exprs(APERSDT, APEREDT),
  join_type = "all",
  filter_join = APERSDT <= ASTDT & ASTDT <= APEREDT
) %>%
  select(USUBJID, ASTDT, APERSDT, APEREDT, APERIOD)
#> # A tibble: 6 × 5
#>   USUBJID ASTDT      APERSDT    APEREDT    APERIOD
#>   <chr>   <date>     <date>     <date>       <int>
#> 1 1       2021-01-01 NA         NA              NA
#> 2 1       2021-01-05 2021-01-04 2021-02-06       1
#> 3 1       2021-02-05 2021-01-04 2021-02-06       1
#> 4 1       2021-03-05 2021-02-07 2021-03-07       2
#> 5 1       2021-04-05 NA         NA              NA
#> 6 2       2021-02-15 2021-02-02 2021-03-02       1

Further examples

Further example usages of this function can be found in the Generic Derivations vignette.

Equivalent examples for using the exist_flag, true_value, false_value, missing_values and check_type arguments can be found in derive_vars_merged().

See Also

derive_var_joined_exist_flag(), filter_joined()

General Derivation Functions for all ADaMs that returns variable appended to dataset: derive_var_extreme_flag(), derive_var_joined_exist_flag(), derive_var_merged_ef_msrc(), derive_var_merged_exist_flag(), derive_var_merged_summary(), derive_var_obs_number(), derive_var_relative_flag(), derive_vars_cat(), derive_vars_computed(), derive_vars_joined_summary(), derive_vars_merged(), derive_vars_merged_lookup(), derive_vars_transposed()

admiral documentation built on June 26, 2025, 1:08 a.m.