Derive Confirmation Flag

This function is deprecated, please use derive_var_joined_exist_flag() instead.

Usage

derive_var_confirmation_flag(
  dataset,
  by_vars,
  order,
  new_var,
  tmp_obs_nr_var = NULL,
  join_vars,
  join_type,
  first_cond = NULL,
  filter,
  true_value = "Y",
  false_value = NA_character_,
  check_type = "warning"
)

Arguments

dataset

Input dataset

The variables specified by the by_vars and join_vars parameter are expected.

by_vars

By variables

The specified variables are used as by variables for joining the input dataset with itself.

order

Order

The observations are ordered by the specified 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 and 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, first_cond). It is not included in the output dataset. It can 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 = 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 to be flagged. Thus join_type = "after" could be used.

Whereas, sometimes you might allow for confirmatory observations to occur prior to the observation to be flagged. For example, to flag 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

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 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

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".

true_value

Value of new_var for flagged observations

Default: "Y"

false_value

Value of new_var for observations not flagged

Default: NA_character_

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.

Default: "warning"

Permitted Values: "none", "warning", "error"

Value

The input dataset with the variable specified by new_var added.

Details

The following steps are performed to produce the output dataset.

Step 1

The input dataset is joined with itself by the variables specified for by_vars. From the right hand side of the join only the variables specified for join_vars are kept. The suffix ".join" is added to these variables.

For example, for by_vars = USUBJID, join_vars = exprs(AVISITN, AVALC) and input 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 is specified, for each observation of the input dataset the joined dataset is restricted to observations up to the first observation where first_cond 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.

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.

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.