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Derives a Flag Based on an Existing Flag

Source: R/derive_var_joined_exist_flag.R
derive_var_joined_exist_flag.Rd

Derive a flag which depends on other observations of the dataset. For example, flagging events which need to be confirmed by a second event.

Usage 

derive_var_joined_exist_flag(
  dataset,
  dataset_add,
  by_vars,
  order = NULL,
  new_var,
  tmp_obs_nr_var = NULL,
  join_vars,
  join_type,
  first_cond_lower = NULL,
  first_cond_upper = NULL,
  filter_add = NULL,
  filter_join,
  true_value = "Y",
  false_value = NA_character_,
  check_type = "warning"
)

Arguments

dataset

Input dataset

The variables specified by the by_vars and join_vars arguments 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 for by_vars, join_vars, and order are expected.

Permitted values

a dataset, i.e., a data.frame or tibble

Default value

none

by_vars

Grouping variables

The specified variables are used for joining the input dataset (dataset) with the additional dataset (dataset_add).

Permitted values

list of variables created by exprs(), e.g., exprs(USUBJID, VISIT)

Default value

none

order

Order

The observations are ordered by the specified order if join_type = "after", join_type = "before", first_cond_lower, first_cond_upper, or tmp_obs_nr_var are specified.

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_var

New variable

The specified variable is added to the input dataset.

Permitted values

an unquoted symbol, e.g., AVAL

Default value

none

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 is not included in the output dataset. It can also be used to flag consecutive observations or the last observation (see last example below).

Permitted values

an unquoted symbol, e.g., AVAL

Default value

NULL

join_vars

Variables to keep from joined dataset

The variables needed from the other observations should be specified for this parameter. The specified variables are added to the joined dataset with suffix ".join". For example to flag all observations with AVALC == "Y" and AVALC == "Y" for at least one subsequent visit join_vars = exprs(AVALC, AVISITN) and filter_join = AVALC == "Y" & AVALC.join == "Y" & AVISITN < AVISITN.join could be specified.

The *.join variables are not included in the output dataset.

Permitted values

list of variables created by exprs(), e.g., exprs(USUBJID, VISIT)

Default value

none

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

first_cond_lower

Condition for selecting range of data (before)

If this argument is specified, the other observations are restricted from the first 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, i.e., the observation is not flagged.

This parameter 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 last example 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, i.e., the observation is not flagged.

This parameter 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 third example below.

Permitted values

an unquoted condition, e.g., AVISIT == "BASELINE"

Default value

NULL

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

filter_join

Condition for selecting observations

The filter is applied to the joined dataset for flagging the confirmed observations. The condition can include summary functions like all() or any(). The joined dataset is grouped by the original observations. I.e., the summary function are applied to all observations up to the confirmation observation. For example, filter_join = AVALC == "CR" & all(AVALC.join %in% c("CR", "NE")) & count_vals(var = AVALC.join, val = "NE") <= 1 selects observations with response "CR" and for all observations up to the confirmation observation the response is "CR" or "NE" and there is at most one "NE".

Permitted values

an unquoted condition, e.g., AVISIT == "BASELINE"

Default value

none

true_value

Value of new_var for flagged observations

Permitted values

a character scalar, i.e., a character vector of length one

Default value

"Y"

false_value

Value of new_var for observations not flagged

Permitted values

a character scalar, i.e., a character vector of length one

Default value

NA_character_

check_type

Check uniqueness?

If "message", "warning", or "error" is specified, the specified message is issued if the observations of the input dataset are not unique with respect to the by variables and the order.

Permitted values

"none", "message", "warning", "error"

Default value

"warning"

Value

The input dataset with the variable specified by new_var added.

Details

An example usage might be flagging if a patient received two required medications within a certain timeframe of each other.

In the oncology setting, for example, the function could be used to flag if a response value can be confirmed by an other assessment. This is commonly used in endpoints such as best overall response.

The following steps are performed to produce the output dataset.

Step 1

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

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

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

The input dataset (dataset) is joined with the restricted additional dataset by the variables specified for by_vars. From the additional dataset only the variables specified for join_vars are kept. The suffix ".join" is added to those variables which also exist in the input dataset.

For example, for by_vars = USUBJID, join_vars = exprs(AVISITN, AVALC) and input dataset and additional dataset

# A tibble: 2 x 4
USUBJID AVISITN AVALC  AVAL
<chr>     <dbl> <chr> <dbl>
1             1 Y         1
1             2 N         0

the joined dataset is

A tibble: 4 x 6
USUBJID AVISITN AVALC  AVAL AVISITN.join AVALC.join
<chr>     <dbl> <chr> <dbl>        <dbl> <chr>
1             1 Y         1            1 Y
1             1 Y         1            2 N
1             2 N         0            1 Y
1             2 N         0            2 N

Step 2

The joined dataset is restricted to observations with respect to join_type and order.

The dataset from the example in the previous step with join_type = "after" and order = exprs(AVISITN) is restricted to

A tibble: 4 x 6
USUBJID AVISITN AVALC  AVAL AVISITN.join AVALC.join
<chr>     <dbl> <chr> <dbl>        <dbl> <chr>
1             1 Y         1            2 N

Step 3

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 examples see the "Examples" section.

Step 4

The joined dataset is grouped by the observations from the input dataset and restricted to the observations fulfilling the condition specified by filter_join.

Step 5

The first observation of each group is selected.

Step 6

The variable specified by new_var is added to the input dataset. It is set to true_value for all observations which were selected in the previous step. For the other observations it is set to false_value.

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.

See also

filter_joined(), derive_vars_joined()

General Derivation Functions for all ADaMs that returns variable appended to dataset: derive_var_extreme_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(), derive_vars_joined_summary(), derive_vars_merged(), derive_vars_merged_lookup(), derive_vars_transposed()

Examples

Flag records considering other records (filter_join, join_vars)

In this example, records with a duration longer than 30 and where a COVID AE (ACOVFL == "Y") occurred before or up to seven days after the record should be flagged. The condition for flagging the records is specified by the filter_join argument. Variables from the other records are referenced by variable names with the suffix .join. These variables have to be specified for the join_vars argument. As records before and after the current record should be considered, join_type = "all" is specified.

library(tibble)

adae <- tribble(
  ~USUBJID, ~ADY, ~ACOVFL, ~ADURN,
  "1",        10, "N",          1,
  "1",        21, "N",         50,
  "1",        23, "Y",         14,
  "1",        32, "N",         31,
  "1",        42, "N",         20,
  "2",        11, "Y",         13,
  "2",        23, "N",          2,
  "3",        13, "Y",         12,
  "4",        14, "N",         32,
  "4",        21, "N",         41
)

derive_var_joined_exist_flag(
  adae,
  dataset_add = adae,
  new_var = ALCOVFL,
  by_vars = exprs(USUBJID),
  join_vars = exprs(ACOVFL, ADY),
  join_type = "all",
  filter_join = ADURN > 30 & ACOVFL.join == "Y" & ADY.join <= ADY + 7
)
#> # A tibble: 10 × 5
#>    USUBJID   ADY ACOVFL ADURN ALCOVFL
#>    <chr>   <dbl> <chr>  <dbl> <chr>
#>  1 1          10 N          1 <NA>
#>  2 1          21 N         50 Y
#>  3 1          23 Y         14 <NA>
#>  4 1          32 N         31 Y
#>  5 1          42 N         20 <NA>
#>  6 2          11 Y         13 <NA>
#>  7 2          23 N          2 <NA>
#>  8 3          13 Y         12 <NA>
#>  9 4          14 N         32 <NA>
#> 10 4          21 N         41 <NA>

Considering only records after the current one (join_type = "after", true_value, false_value)

In this example, records with AVALC == "Y" and AVALC == "Y" at a subsequent visit should be flagged. join_type = "after" is specified to consider only records after the current one. Please note that the order argument must be specified, as otherwise it is not possible to determine which records are after the current record.

Please note that a numeric flag is created here by specifying the true_value and the false_value argument.

data <- tribble(
  ~USUBJID, ~AVISITN, ~AVALC,
  "1",      1,        "Y",
  "1",      2,        "N",
  "1",      3,        "Y",
  "1",      4,        "N",
  "2",      1,        "Y",
  "2",      2,        "N",
  "3",      1,        "Y",
  "4",      1,        "N",
  "4",      2,        "N",
)

derive_var_joined_exist_flag(
  data,
  dataset_add = data,
  by_vars = exprs(USUBJID),
  new_var = CONFFLN,
  join_vars = exprs(AVALC, AVISITN),
  join_type = "after",
  order = exprs(AVISITN),
  filter_join = AVALC == "Y" & AVALC.join == "Y",
  true_value = 1,
  false_value = 0
)
#> # A tibble: 9 × 4
#>   USUBJID AVISITN AVALC CONFFLN
#>   <chr>     <dbl> <chr>   <dbl>
#> 1 1             1 Y           1
#> 2 1             2 N           0
#> 3 1             3 Y           0
#> 4 1             4 N           0
#> 5 2             1 Y           0
#> 6 2             2 N           0
#> 7 3             1 Y           0
#> 8 4             1 N           0
#> 9 4             2 N           0

Considering a range of records only (first_cond_lower, first_cond_upper)

Consider the following data.

myd <- tribble(
  ~subj, ~day, ~val,
  "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, "++"
)

To flag "0" where all results from the first "++" before the "0" up to the "0" (excluding the "0") are "+" or "++" the first_cond_lower argument and join_type = "before" are specified.

derive_var_joined_exist_flag(
  myd,
  dataset_add = myd,
  by_vars = exprs(subj),
  order = exprs(day),
  new_var = flag,
  join_vars = exprs(val),
  join_type = "before",
  first_cond_lower = val.join == "++",
  filter_join = val == "0" & all(val.join %in% c("+", "++"))
)
#> # A tibble: 12 × 4
#>    subj    day val   flag
#>    <chr> <dbl> <chr> <chr>
#>  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     Y
#> 11 2         5 -     <NA>
#> 12 2         6 ++    <NA>

To flag "0" where all results from the "0" (excluding the "0") up to the first "++" after the "0" are "+" or "++" the first_cond_upper argument and join_type = "after" are specified.

derive_var_joined_exist_flag(
  myd,
  dataset_add = myd,
  by_vars = exprs(subj),
  order = exprs(day),
  new_var = flag,
  join_vars = exprs(val),
  join_type = "after",
  first_cond_upper = val.join == "++",
  filter_join = val == "0" & all(val.join %in% c("+", "++"))
)
#> # A tibble: 12 × 4
#>    subj    day val   flag
#>    <chr> <dbl> <chr> <chr>
#>  1 1         1 ++    <NA>
#>  2 1         2 -     <NA>
#>  3 1         3 0     Y
#>  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>

Considering only records up to a condition (first_cond_upper)

In this example from deriving confirmed response in oncology, the records with

  • AVALC == "CR",

  • AVALC == "CR" at a subsequent visit,

  • only "CR" or "NE" in between, and

  • at most one "NE" in between

should be flagged. The other records to be considered are restricted to those up to the first occurrence of "CR" by specifying the first_cond_upper argument. The count_vals() function is used to count the "NE"s for the last condition.

data <- tribble(
  ~USUBJID, ~AVISITN, ~AVALC,
  "1",      1,        "PR",
  "1",      2,        "CR",
  "1",      3,        "NE",
  "1",      4,        "CR",
  "1",      5,        "NE",
  "2",      1,        "CR",
  "2",      2,        "PR",
  "2",      3,        "CR",
  "3",      1,        "CR",
  "4",      1,        "CR",
  "4",      2,        "NE",
  "4",      3,        "NE",
  "4",      4,        "CR",
  "4",      5,        "PR"
)

derive_var_joined_exist_flag(
  data,
  dataset_add = data,
  by_vars = exprs(USUBJID),
  join_vars = exprs(AVALC),
  join_type = "after",
  order = exprs(AVISITN),
  new_var = CONFFL,
  first_cond_upper = AVALC.join == "CR",
  filter_join = AVALC == "CR" & all(AVALC.join %in% c("CR", "NE")) &
    count_vals(var = AVALC.join, val = "NE") <= 1
)
#> # A tibble: 14 × 4
#>    USUBJID AVISITN AVALC CONFFL
#>    <chr>     <dbl> <chr> <chr>
#>  1 1             1 PR    <NA>
#>  2 1             2 CR    Y
#>  3 1             3 NE    <NA>
#>  4 1             4 CR    <NA>
#>  5 1             5 NE    <NA>
#>  6 2             1 CR    <NA>
#>  7 2             2 PR    <NA>
#>  8 2             3 CR    <NA>
#>  9 3             1 CR    <NA>
#> 10 4             1 CR    <NA>
#> 11 4             2 NE    <NA>
#> 12 4             3 NE    <NA>
#> 13 4             4 CR    <NA>
#> 14 4             5 PR    <NA>

Considering order of values (min_cond(), max_cond())

In this example from deriving confirmed response in oncology, records with

  • AVALC == "PR",

  • AVALC == "CR" or AVALC == "PR" at a subsequent visit at least 20 days later,

  • only "CR", "PR", or "NE" in between,

  • at most one "NE" in between, and

  • "CR" is not followed by "PR"

should be flagged. The last condition is realized by using min_cond() and max_cond(), ensuring that the first occurrence of "CR" is after the last occurrence of "PR". The second call to count_vals() in the condition is required to cover the case of no "CR"s (the min_cond() call returns NA then).

data <- tribble(
  ~USUBJID, ~ADY, ~AVALC,
  "1",         6, "PR",
  "1",        12, "CR",
  "1",        24, "NE",
  "1",        32, "CR",
  "1",        48, "PR",
  "2",         3, "PR",
  "2",        21, "CR",
  "2",        33, "PR",
  "3",        11, "PR",
  "4",         7, "PR",
  "4",        12, "NE",
  "4",        24, "NE",
  "4",        32, "PR",
  "4",        55, "PR"
)

derive_var_joined_exist_flag(
  data,
  dataset_add = data,
  by_vars = exprs(USUBJID),
  join_vars = exprs(AVALC, ADY),
  join_type = "after",
  order = exprs(ADY),
  new_var = CONFFL,
  first_cond_upper = AVALC.join %in% c("CR", "PR") & ADY.join - ADY >= 20,
  filter_join = AVALC == "PR" &
    all(AVALC.join %in% c("CR", "PR", "NE")) &
    count_vals(var = AVALC.join, val = "NE") <= 1 &
    (
      min_cond(var = ADY.join, cond = AVALC.join == "CR") >
        max_cond(var = ADY.join, cond = AVALC.join == "PR") |
        count_vals(var = AVALC.join, val = "CR") == 0
    )
)
#> # A tibble: 14 × 4
#>    USUBJID   ADY AVALC CONFFL
#>    <chr>   <dbl> <chr> <chr>
#>  1 1           6 PR    <NA>
#>  2 1          12 CR    <NA>
#>  3 1          24 NE    <NA>
#>  4 1          32 CR    <NA>
#>  5 1          48 PR    <NA>
#>  6 2           3 PR    <NA>
#>  7 2          21 CR    <NA>
#>  8 2          33 PR    <NA>
#>  9 3          11 PR    <NA>
#> 10 4           7 PR    <NA>
#> 11 4          12 NE    <NA>
#> 12 4          24 NE    <NA>
#> 13 4          32 PR    Y
#> 14 4          55 PR    <NA>

Considering the order of records (tmp_obs_nr_var)

In this example, the records with CRIT1FL == "Y" at two consecutive visits or at the last visit should be flagged. A temporary order variable is created by specifying the tmp_obs_nr_var argument. Then it is used in filter_join. The temporary variable doesn't need to be specified for join_vars.

data <- tribble(
  ~USUBJID, ~AVISITN, ~CRIT1FL,
  "1",      1,        "Y",
  "1",      2,        "N",
  "1",      3,        "Y",
  "1",      5,        "N",
  "2",      1,        "Y",
  "2",      3,        "Y",
  "2",      5,        "N",
  "3",      1,        "Y",
  "4",      1,        "Y",
  "4",      2,        "N",
)

derive_var_joined_exist_flag(
  data,
  dataset_add = data,
  by_vars = exprs(USUBJID),
  new_var = CONFFL,
  tmp_obs_nr_var = tmp_obs_nr,
  join_vars = exprs(CRIT1FL),
  join_type = "all",
  order = exprs(AVISITN),
  filter_join = CRIT1FL == "Y" & CRIT1FL.join == "Y" &
    (tmp_obs_nr + 1 == tmp_obs_nr.join | tmp_obs_nr == max(tmp_obs_nr.join))
)
#> # A tibble: 10 × 4
#>    USUBJID AVISITN CRIT1FL CONFFL
#>    <chr>     <dbl> <chr>   <chr>
#>  1 1             1 Y       <NA>
#>  2 1             2 N       <NA>
#>  3 1             3 Y       <NA>
#>  4 1             5 N       <NA>
#>  5 2             1 Y       Y
#>  6 2             3 Y       <NA>
#>  7 2             5 N       <NA>
#>  8 3             1 Y       Y
#>  9 4             1 Y       <NA>
#> 10 4             2 N       <NA>

Flag each dose which is lower than the previous dose (tmp_obs_nr_var) 

ex <- tribble(
  ~USUBJID, ~EXSTDTM,           ~EXDOSE,
  "1",      "2024-01-01T08:00",       2,
  "1",      "2024-01-02T08:00",       4,
  "2",      "2024-01-01T08:30",       1,
  "2",      "2024-01-02T08:30",       4,
  "2",      "2024-01-03T08:30",       3,
  "2",      "2024-01-04T08:30",       2,
  "2",      "2024-01-05T08:30",       2
)

derive_var_joined_exist_flag(
  ex,
  dataset_add = ex,
  by_vars = exprs(USUBJID),
  order = exprs(EXSTDTM),
  new_var = DOSREDFL,
  tmp_obs_nr_var = tmp_dose_nr,
  join_vars = exprs(EXDOSE),
  join_type = "before",
  filter_join = (
    tmp_dose_nr == tmp_dose_nr.join + 1 # Look only at adjacent doses
    & EXDOSE > 0 & EXDOSE.join > 0 # Both doses are valid
    & EXDOSE < EXDOSE.join # Dose is lower than previous
  )
)
#> # A tibble: 7 × 4
#>   USUBJID EXSTDTM          EXDOSE DOSREDFL
#>   <chr>   <chr>             <dbl> <chr>
#> 1 1       2024-01-01T08:00      2 <NA>
#> 2 1       2024-01-02T08:00      4 <NA>
#> 3 2       2024-01-01T08:30      1 <NA>
#> 4 2       2024-01-02T08:30      4 <NA>
#> 5 2       2024-01-03T08:30      3 Y
#> 6 2       2024-01-04T08:30      2 Y
#> 7 2       2024-01-05T08:30      2 <NA>

Derive definitive deterioration flag

In this example a definitive deterioration flag should be derived as any deterioration (CHGCAT1 = "Worsened") by parameter that is not followed by a non-deterioration. Please note that join_type = "after" can't by used here, as otherwise the last record wouldn't be flagged.

adqs <- tribble(
  ~USUBJID, ~PARAMCD, ~ADY, ~CHGCAT1,
  "1",      "QS1",      10, "Improved",
  "1",      "QS1",      21, "Improved",
  "1",      "QS1",      23, "Improved",
  "1",      "QS2",      32, "Worsened",
  "1",      "QS2",      42, "Improved",
  "2",      "QS1",      11, "Worsened",
  "2",      "QS1",      24, "Worsened"
)

derive_var_joined_exist_flag(
  adqs,
  dataset_add = adqs,
  new_var = DDETERFL,
  by_vars = exprs(USUBJID, PARAMCD),
  join_vars = exprs(CHGCAT1, ADY),
  join_type = "all",
  filter_join = all(CHGCAT1.join == "Worsened" | ADY > ADY.join)
)
#> # A tibble: 7 × 5
#>   USUBJID PARAMCD   ADY CHGCAT1  DDETERFL
#>   <chr>   <chr>   <dbl> <chr>    <chr>
#> 1 1       QS1        10 Improved <NA>
#> 2 1       QS1        21 Improved <NA>
#> 3 1       QS1        23 Improved <NA>
#> 4 1       QS2        32 Worsened <NA>
#> 5 1       QS2        42 Improved <NA>
#> 6 2       QS1        11 Worsened Y
#> 7 2       QS1        24 Worsened Y

Handling duplicates (check_type)

If the order argument is used, it is checked if the records are unique with respect to by_vars and order. Consider for example the derivation of CONFFL which flags records with AVALC == "Y" which are confirmed at a subsequent visit.

data <- tribble(
  ~USUBJID, ~AVISITN, ~ADY, ~AVALC,
  "1",      1,           1, "Y",
  "1",      2,           8, "N",
  "1",      3,          15, "Y",
  "1",      4,          22, "N",
  "2",      1,           1, "Y",
  "2",      2,           8, "Y",
  "2",      2,          10, "Y"
)

derive_var_joined_exist_flag(
  data,
  dataset_add = data,
  by_vars = exprs(USUBJID),
  new_var = CONFFL,
  join_vars = exprs(AVALC, AVISITN),
  join_type = "after",
  order = exprs(AVISITN),
  filter_join = AVALC == "Y" & AVALC.join == "Y"
)
#> # A tibble: 7 × 5
#>   USUBJID AVISITN   ADY AVALC CONFFL
#>   <chr>     <dbl> <dbl> <chr> <chr>
#> 1 1             1     1 Y     Y
#> 2 1             2     8 N     <NA>
#> 3 1             3    15 Y     <NA>
#> 4 1             4    22 N     <NA>
#> 5 2             1     1 Y     Y
#> 6 2             2     8 Y     <NA>
#> 7 2             2    10 Y     <NA>
#> Warning: Dataset `dataset` contains duplicate records with respect to `USUBJID` and
#> `AVISITN`
#> ℹ Run `admiral::get_duplicates_dataset()` to access the duplicate records
#> Warning: Dataset `dataset_add` contains duplicate records with respect to `USUBJID` and
#> `AVISITN`
#> ℹ Run `admiral::get_duplicates_dataset()` to access the duplicate records

The records for USUBJID == "2" are not unique with respect to USUBJID and AVISITN. Thus a warning is issued. The duplicates can be accessed by calling get_duplicates_dataset():

get_duplicates_dataset()
#> Duplicate records with respect to `USUBJID` and `AVISITN`.
#> # A tibble: 2 × 4
#>   USUBJID AVISITN   ADY AVALC
#> * <chr>     <dbl> <dbl> <chr>
#> 1 2             2     8 Y
#> 2 2             2    10 Y

In this example, confirmation is required at a subsequent visit. Please note that the first record for subject "2" at visit 2 is not flagged. Thus the warning can be suppressed by specifying check_type = "none".

derive_var_joined_exist_flag(
  data,
  dataset_add = data,
  by_vars = exprs(USUBJID),
  new_var = CONFFL,
  join_vars = exprs(AVALC, AVISITN),
  join_type = "after",
  order = exprs(AVISITN),
  filter_join = AVALC == "Y" & AVALC.join == "Y",
  check_type = "none"
)
#> # A tibble: 7 × 5
#>   USUBJID AVISITN   ADY AVALC CONFFL
#>   <chr>     <dbl> <dbl> <chr> <chr>
#> 1 1             1     1 Y     Y
#> 2 1             2     8 N     <NA>
#> 3 1             3    15 Y     <NA>
#> 4 1             4    22 N     <NA>
#> 5 2             1     1 Y     Y
#> 6 2             2     8 Y     <NA>
#> 7 2             2    10 Y     <NA>