derive_vars_joined_summary: Summarize Variables from an Additional Dataset Based on...

In admiral: ADaM in R Asset Library

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

Description

The function summarizes variables from an additional dataset and adds the summarized values as new variables to the input dataset. The selection of the observations from the additional dataset can depend on variables from both datasets. For example, all doses before the current observation can be selected and the sum be added to the input dataset.

Usage

derive_vars_joined_summary(
  dataset,
  dataset_add,
  by_vars = NULL,
  order = NULL,
  new_vars,
  tmp_obs_nr_var = NULL,
  join_vars = NULL,
  join_type,
  filter_add = NULL,
  first_cond_lower = NULL,
  first_cond_upper = NULL,
  filter_join = NULL,
  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 (optionally named) variables created by exprs(), e.g., exprs(USUBJID, ADY = ASTDY) Default value NULL
order	Sort order The specified variables are used to determine the order of the records if first_cond_lower or first_cond_upper is specified or if join_type equals "before" or "after". 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 expressions created by exprs(), e.g., exprs(ADT, desc(AVAL)) or NULL Default value NULL
new_vars	Variables to add The new variables can be defined by named expressions, i.e., ⁠new_vars = exprs(<new variable> = <value>)⁠. The value must be defined such that it results in a single record per by group, e.g., by using a summary function like mean(), sum(), ... Permitted values list of named expressions created by exprs(), e.g., exprs(CUMDOSA = sum(AVAL, na.rm = TRUE), AVALU = "ml") Default value none
tmp_obs_nr_var	Temporary observation number The specified variable is added to the input dataset (dataset) and the restricted additional dataset (dataset_add after applying filter_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 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(EXSTDT = 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 variables are not included in the output dataset. Permitted values list of variables or named expressions created by exprs(), e.g., exprs(EXSTDY, EXSTDTM = convert_dtc_to_dtm(EXSTDTC)) 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. 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 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. 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 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. 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 last example 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
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 input dataset (dataset) or the restricted additional dataset (dataset_add after applying filter_add) are not unique with respect to the by variables and the order. The uniqueness is checked only if tmp_obs_nr_var, first_cond_lower, or first_cond_upper is specified or join_type equals "before" or "after". 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 last example in the "Examples" section.

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

7. The variables specified for new_vars are created 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 derived from the additional dataset (dataset_add).

Examples

The examples focus on the functionality specific to this function. For examples of functionality common to all "joined" functions like filter_join, filter_add, join_vars, ... please see the examples of derive_vars_joined().

Derive cumulative dose before event (CUMDOSA)

Deriving the cumulative actual dose up to the day of the adverse event in the ADAE dataset.

• USUBJID is specified for by_vars to join the ADAE and the ADEX dataset by subject.

• filter_join is specified to restrict the ADEX dataset to the days up to the adverse event. ADY.join refers to the study day in ADEX.

• The new variable CUMDOSA is defined by the new_vars argument. It is set to the sum of AVAL.

• As ADY from ADEX is used in filter_join (but not in new_vars), it needs to be specified for join_vars.

• The join_type is set to "all" to consider all records in the joined dataset. join_type = "before" can't by used here because then doses at the same day as the adverse event would be excluded.

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

adex <- tribble(
  ~USUBJID, ~ADY, ~AVAL,
  "1",         1,    10,
  "1",         8,    20,
  "1",        15,    10,
  "2",         8,     5
)

adae <- tribble(
  ~USUBJID, ~ADY, ~AEDECOD,
  "1",         2, "Fatigue",
  "1",         9, "Influenza",
  "1",        15, "Theft",
  "1",        15, "Fatigue",
  "2",         4, "Parasomnia",
  "3",         2, "Truancy"
)

derive_vars_joined_summary(
  dataset = adae,
  dataset_add = adex,
  by_vars = exprs(USUBJID),
  filter_join = ADY.join <= ADY,
  join_type = "all",
  join_vars = exprs(ADY),
  new_vars = exprs(CUMDOSA = sum(AVAL, na.rm = TRUE))
)
#> # A tibble: 6 × 4
#>   USUBJID   ADY AEDECOD    CUMDOSA
#>   <chr>   <dbl> <chr>        <dbl>
#> 1 1           2 Fatigue         10
#> 2 1           9 Influenza       30
#> 3 1          15 Theft           40
#> 4 1          15 Fatigue         40
#> 5 2           4 Parasomnia      NA
#> 6 3           2 Truancy         NA

Define values for records without records in the additional dataset (missing_values)

By default, the new variables are set to NA for records without matching records in the restricted additional dataset. This can be changed by specifying the missing_values argument.

derive_vars_joined_summary(
  dataset = adae,
  dataset_add = adex,
  by_vars = exprs(USUBJID),
  filter_join = ADY.join <= ADY,
  join_type = "all",
  join_vars = exprs(ADY),
  new_vars = exprs(CUMDOSE = sum(AVAL, na.rm = TRUE)),
  missing_values = exprs(CUMDOSE = 0)
)
#> # A tibble: 6 × 4
#>   USUBJID   ADY AEDECOD    CUMDOSE
#>   <chr>   <dbl> <chr>        <dbl>
#> 1 1           2 Fatigue         10
#> 2 1           9 Influenza       30
#> 3 1          15 Theft           40
#> 4 1          15 Fatigue         40
#> 5 2           4 Parasomnia       0
#> 6 3           2 Truancy          0

Selecting records (join_type = "before", join_type = "after")

The join_type argument can be used to select records from the additional dataset. For example, if join_type = "before" is specified, only records before the current observation are selected. If join_type = "after" is specified, only records after the current observation are selected.

To illustrate this, a variable (SELECTED_DAYS) is derived which contains the selected days.

mydata <- tribble(
  ~DAY,
  1,
  2,
  3,
  4,
  5
)

derive_vars_joined_summary(
  mydata,
  dataset_add = mydata,
  order = exprs(DAY),
  join_type = "before",
  new_vars = exprs(SELECTED_DAYS = paste(DAY, collapse = ", "))
)
#> # A tibble: 5 × 2
#>     DAY SELECTED_DAYS
#>   <dbl> <chr>        
#> 1     1 <NA>         
#> 2     2 1            
#> 3     3 1, 2         
#> 4     4 1, 2, 3      
#> 5     5 1, 2, 3, 4   

derive_vars_joined_summary(
  mydata,
  dataset_add = mydata,
  order = exprs(DAY),
  join_type = "after",
  new_vars = exprs(SELECTED_DAYS = paste(DAY, collapse = ", "))
)
#> # A tibble: 5 × 2
#>     DAY SELECTED_DAYS
#>   <dbl> <chr>        
#> 1     1 2, 3, 4, 5   
#> 2     2 3, 4, 5      
#> 3     3 4, 5         
#> 4     4 5            
#> 5     5 <NA>         

Selecting records (first_cond_lower, first_cond_upper)

The first_cond_lower and first_cond_upper arguments can be used to restrict the joined dataset to a certain range of records. For example, if first_cond_lower is specified, the joined dataset is restricted to the last observation before the current record where the condition is fulfilled.

Please note:

• If the condition is not fulfilled for any of the records, no records are selected.

• The restriction implied by join_type is applied first.

• If a variable is contained in both dataset and dataset_add like DAY in the example below, DAY refers to the value from dataset and DAY.join to the value from dataset_add.

To illustrate this, a variable (SELECTED_DAYS) is derived which contains the selected days.

derive_vars_joined_summary(
  mydata,
  dataset_add = mydata,
  order = exprs(DAY),
  join_type = "before",
  first_cond_lower = DAY.join == 2,
  new_vars = exprs(SELECTED_DAYS = paste(sort(DAY), collapse = ", "))
)
#> # A tibble: 5 × 2
#>     DAY SELECTED_DAYS
#>   <dbl> <chr>        
#> 1     1 <NA>         
#> 2     2 <NA>         
#> 3     3 2            
#> 4     4 2, 3         
#> 5     5 2, 3, 4      

derive_vars_joined_summary(
  mydata,
  dataset_add = mydata,
  order = exprs(DAY),
  join_type = "after",
  first_cond_upper = DAY.join == 4,
  new_vars = exprs(SELECTED_DAYS = paste(DAY, collapse = ", "))
)
#> # A tibble: 5 × 2
#>     DAY SELECTED_DAYS
#>   <dbl> <chr>        
#> 1     1 2, 3, 4      
#> 2     2 3, 4         
#> 3     3 4            
#> 4     4 <NA>         
#> 5     5 <NA>         

derive_vars_joined_summary(
  mydata,
  dataset_add = mydata,
  order = exprs(DAY),
  join_type = "all",
  first_cond_lower = DAY.join == 2,
  first_cond_upper = DAY.join == 4,
  new_vars = exprs(SELECTED_DAYS = paste(sort(DAY), collapse = ", "))
)
#> # A tibble: 5 × 2
#>     DAY SELECTED_DAYS
#>   <dbl> <chr>        
#> 1     1 2, 3, 4      
#> 2     2 2, 3, 4      
#> 3     3 2, 3, 4      
#> 4     4 2, 3, 4      
#> 5     5 2, 3, 4      

Derive weekly score if enough assessments are available

For each planned visit the average score within the week before the visit should be derived if at least three assessments are available.

Please note that the condition for the number of assessments is specified in new_vars and not in filter_join. This is because the number of assessments within the week before the visit should be counted but not the number of assessments available for the subject.

planned_visits <- tribble(
  ~AVISIT,  ~ADY,
  "WEEK 1",    8,
  "WEEK 4",   29,
  "WEEK 8",   57
  ) %>%
  mutate(USUBJID = "1", .before = AVISIT)

adqs <- tribble(
  ~ADY, ~AVAL,
     1,    10,
     2,    12,
     4,     9,
     5,     9,
     7,    10,
    25,    11,
    27,    10,
    29,    10,
    41,     8,
    42,     9,
    44,     5
) %>%
mutate(USUBJID = "1")

derive_vars_joined_summary(
  planned_visits,
  dataset_add = adqs,
  by_vars = exprs(USUBJID),
  filter_join = ADY - 7 <= ADY.join & ADY.join < ADY,
  join_type = "all",
  join_vars = exprs(ADY),
  new_vars = exprs(AVAL = if_else(n() >= 3, mean(AVAL, na.rm = TRUE), NA))
)
#> # A tibble: 3 × 4
#>   USUBJID AVISIT   ADY  AVAL
#>   <chr>   <chr>  <dbl> <dbl>
#> 1 1       WEEK 1     8    10
#> 2 1       WEEK 4    29    NA
#> 3 1       WEEK 8    57    NA

See Also

derive_vars_joined(), derive_var_merged_summary(), 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(), derive_vars_merged(), derive_vars_merged_lookup(), derive_vars_transposed()

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