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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,
  new_var,
  tmp_obs_nr_var = NULL,
  join_vars,
  join_type,
  first_cond = NULL,
  first_cond_lower = NULL,
  first_cond_upper = NULL,
  filter = 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.

dataset_add

Additional dataset

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

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)

order

Order

The observations are ordered by the specified order.

For handling of NAs in sorting variables see Sort Order.

new_var

New variable

The specified variable is added to the input dataset.

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. 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).

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.

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"

first_cond

Condition for selecting range of data

[Deprecated]

This argument is deprecated, please use first_cond_upper instead.

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.

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.

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.

filter

Condition for selecting observations

[Deprecated]

This argument is deprecated, please use filter_join instead.

The filter is applied to the joined dataset for flagging the confirmed observations. The condition can include summary functions. 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 = 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".

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: a condition

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".

true_value

Value of new_var for flagged observations

false_value

Value of new_var for observations not flagged

check_type

Check uniqueness?

If "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", "warning", "error"

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 an example see the last example in 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.

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_computed(), derive_vars_joined(), derive_vars_merged(), derive_vars_merged_lookup(), derive_vars_transposed()

Examples

library(tibble)

# flag observations with a duration longer than 30 and
# at, after, or up to 7 days before a COVID AE (ACOVFL == "Y")
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",
  order = exprs(ADY),
  filter_join = ADURN > 30 & ACOVFL.join == "Y" & ADY >= ADY.join - 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     

# flag observations with AVALC == "Y" and AVALC == "Y" at one subsequent visit
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 = CONFFL,
  join_vars = exprs(AVALC, AVISITN),
  join_type = "after",
  order = exprs(AVISITN),
  filter_join = AVALC == "Y" & AVALC.join == "Y" & AVISITN < AVISITN.join
)
#> # A tibble: 9 × 4
#>   USUBJID AVISITN AVALC CONFFL
#>   <chr>     <dbl> <chr> <chr> 
#> 1 1             1 Y     Y     
#> 2 1             2 N     NA    
#> 3 1             3 Y     NA    
#> 4 1             4 N     NA    
#> 5 2             1 Y     NA    
#> 6 2             2 N     NA    
#> 7 3             1 Y     NA    
#> 8 4             1 N     NA    
#> 9 4             2 N     NA    

# select observations with AVALC == "CR", AVALC == "CR" at a subsequent visit,
# only "CR" or "NE" in between, and at most one "NE" in between
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    

# flag observations 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"
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    

# flag observations with CRIT1FL == "Y" at two consecutive visits or at the last visit
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    

# first_cond_lower and first_cond_upper argument
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, "++"
)

# flag "0" where all results from the first "++" before the "0" up to the "0"
# (excluding the "0") are "+" or "++"
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   

# flag "0" where all results from the "0" (excluding the "0") up to the first
# "++" after the "0" are "+" or "++"
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