# Add the First or Last Observation for Each By Group as New Records Add the first or last observation for each by group as new observations. The new observations can be selected from the additional dataset. This function can be used for adding the maximum or minimum value as a separate visit. All variables of the selected observation are kept. This distinguishes `derive_extreme_records()` from `derive_summary_records()`, where only the by variables are populated for the new records. ```r derive_extreme_records( dataset = NULL, dataset_add, dataset_ref = NULL, by_vars = NULL, order = NULL, mode = NULL, filter_add = NULL, check_type = "warning", exist_flag = NULL, true_value = "Y", false_value = NA_character_, keep_source_vars = exprs(everything()), set_values_to ) ``` ## Arguments - `dataset`: Input dataset If the argument is not specified (or set to `NULL`), a new dataset is created. Otherwise, the new records are appended to the specified dataset. - **Permitted values**: a dataset, i.e., a `data.frame` or tibble - **Default value**: `NULL` - `dataset_add`: Additional dataset The additional dataset, which determines the by groups returned in the input dataset, based on the groups that exist in this dataset after being subset by `filter_add`. The variables specified in the `by_vars` and `filter_add` parameters are expected in this dataset. If `mode` and `order` are specified, the first or last observation within each by group, defined by `by_vars`, is selected. - **Permitted values**: a dataset, i.e., a `data.frame` or tibble - **Default value**: none - `dataset_ref`: Reference dataset The variables specified for `by_vars` are expected. For each observation of the specified dataset a new observation is added to the input dataset. For records which are added from `dataset_ref` because there are no records in `dataset_add` for the by group only those variables are kept which are also in `dataset_add` (and are included in `keep_source_vars`). - **Permitted values**: a dataset, i.e., a `data.frame` or tibble - **Default value**: `NULL` - `by_vars`: Grouping variables If `dataset_ref` is specified, this argument must be specified. - **Permitted values**: list of variables created by `exprs()`, e.g., `exprs(USUBJID, VISIT)` - **Default value**: `NULL` - `order`: Sort order Within each by group the observations are ordered by the specified order. - **Permitted values**: list of variables created by `exprs()`, e.g., `exprs(USUBJID, VISIT)` - **Default value**: none - `mode`: Selection mode (first or last) If `"first"` is specified, the first observation of each by group is added to the input dataset. If `"last"` is specified, the last observation of each by group is added to the input dataset. - **Permitted values**: `"first"`, `"last"` - **Default value**: `NULL` - `filter_add`: Filter for additional dataset (`dataset_add`) Only observations in `dataset_add` fulfilling the specified condition are considered. - **Permitted values**: an unquoted condition, e.g., `AVISIT == "BASELINE"` - **Default value**: `NULL` - `check_type`: Check uniqueness? If `"warning"` or `"error"` is specified, the specified message is issued if the observations of the (restricted) additional dataset are not unique with respect to the by variables and the order. - **Permitted values**: `"none"`, `"message"`, `"warning"`, `"error"` - **Default value**: `"warning"` - `exist_flag`: Existence flag The specified variable is added to the output dataset. For by groups with at least one observation in the additional dataset (`dataset_add`) `exist_flag` is set to the value specified by the `true_value` argument. For all other by groups `exist_flag` is set to the value specified by the `false_value` argument. - **Permitted values**: Variable name - **Default value**: `NULL` - `true_value`: True value For new observations selected from the additional dataset (`dataset_add`), `exist_flag` is set to the specified value. - **Permitted values**: a character scalar, i.e., a character vector of length one - **Default value**: `"Y"` - `false_value`: False value For new observations not selected from the additional dataset (`dataset_add`), `exist_flag` is set to the specified value. - **Permitted values**: a character scalar, i.e., a character vector of length one - **Default value**: `NA_character_` - `keep_source_vars`: Variables to be kept in the new records A named list or tidyselect expressions created by `exprs()` defining the variables to be kept for the new records. The variables specified for `by_vars` and `set_values_to` need not be specified here as they are kept automatically. - **Permitted values**: list of variables or tidyselect expressions created by `exprs()`, e.g., `exprs(DTHDT, starts_with("AST"))` or `exprs(everything)` - **Default value**: `exprs(everything())` - `set_values_to`: Variables to be set The specified variables are set to the specified values for the new observations. Set a list of variables to some specified value for the new records * LHS refer to a variable. * RHS refers to the values to set to the variable. This can be a string, a symbol, a numeric value, an expression or NA. If summary functions are used, the values are summarized by the variables specified for `by_vars`. For example: ``` set_values_to = exprs( AVAL = sum(AVAL), DTYPE = "AVERAGE", ) ``` - **Permitted values**: list of named expressions created by `exprs()`, e.g., `exprs(CUMDOSA = sum(AVAL, na.rm = TRUE), AVALU = "ml")` - **Default value**: none ## Returns The input dataset with the first or last observation of each by group added as new observations. ## Details 1. The additional dataset (`dataset_add`) is restricted as specified by the `filter_add` argument. 2. For each group (with respect to the variables specified for the `by_vars` argument) the first or last observation (with respect to the order specified for the `order` argument and the mode specified for the `mode` argument) is selected. 3. If `dataset_ref` is specified, observations which are in `dataset_ref` but not in the selected records are added. Variables that are common across `dataset_ref`, `dataset_add` and `keep_source_vars()` are also populated for the new observations. 4. The variables specified by the `set_values_to` argument are added to the selected observations. 5. The variables specified by the `keep_source_vars` argument are selected along with the variables specified in `by_vars` and `set_values_to` arguments. 6. The observations are added to input dataset (`dataset`). If no input dataset is provided, a new dataset is created. ## See Also `derive_summary_records()` BDS-Findings Functions for adding Parameters/Records: `default_qtc_paramcd()`, `derive_expected_records()`, `derive_extreme_event()`, `derive_locf_records()`, `derive_param_bmi()`, `derive_param_bsa()`, `derive_param_computed()`, `derive_param_doseint()`, `derive_param_exist_flag()`, `derive_param_exposure()`, `derive_param_framingham()`, `derive_param_map()`, `derive_param_qtc()`, `derive_param_rr()`, `derive_param_wbc_abs()`, `derive_summary_records()` ## Examples ### Add last/first record as new record For each subject the last record should be added as a new visit. * The source dataset for the new records is specified by the `dataset_add` argument. Here it is the same as the input dataset. * The groups for which new records are added are specified by the `by_vars` argument. Here for each **subject** a record should be added. Thus `by_vars = exprs(USUBJID)` is specified. * As there are multiple records per subject, the `mode` and `order` arguments are specified to request that the **last** record is selected when the records are sorted by visit (`AVISITN`). The records are sorted by each by group (`by_vars`) separately, i.e., it is not necessary to add the variables from `by_vars` to `order`. * To avoid duplicates in the output dataset the `set_values_to` argument is specified to set the visit (`AVISIT`) to a special value for the **new** records. ``` library(tibble) library(dplyr, warn.conflicts = FALSE) library(lubridate, warn.conflicts = FALSE) adlb <- tribble( ~USUBJID, ~AVISITN, ~AVAL, "1", 1, 113, "1", 2, 111, "2", 1, 101, "2", 2, NA, "3", 1, NA, ) derive_extreme_records( adlb, dataset_add = adlb, by_vars = exprs(USUBJID), order = exprs(AVISITN), mode = "last", set_values_to = exprs( AVISITN = 99 ) ) #> # A tibble: 8 × 3 #> USUBJID AVISITN AVAL #> #> 1 1 1 113 #> 2 1 2 111 #> 3 2 1 101 #> 4 2 2 NA #> 5 3 1 NA #> 6 1 99 111 #> 7 2 99 NA #> 8 3 99 NA ``` ### Restricting source records (`filter_add`) The source records can be restricted by the `filter_add` argument, e.g., to exclude visits with missing analysis value from selecting for the last visit record: ``` derive_extreme_records( adlb, dataset_add = adlb, filter_add = !is.na(AVAL), by_vars = exprs(USUBJID), order = exprs(AVISITN), mode = "last", set_values_to = exprs( AVISITN = 99 ) ) #> # A tibble: 7 × 3 #> USUBJID AVISITN AVAL #> #> 1 1 1 113 #> 2 1 2 111 #> 3 2 1 101 #> 4 2 2 NA #> 5 3 1 NA #> 6 1 99 111 #> 7 2 99 101 ``` Please note that new records are added only for subjects in the **restricted** source data. Therefore no new record is added for subject `"3"`. ### Adding records for groups not in source (`dataset_ref`) Adding records for groups which are not in the source data can be achieved by specifying a reference dataset by the `dataset_ref` argument. For example, specifying the input dataset for `dataset_ref` below ensures that new records are added also for subject without a valid analysis value: ``` derive_extreme_records( adlb, dataset_add = adlb, filter_add = !is.na(AVAL), dataset_ref = adlb, by_vars = exprs(USUBJID), order = exprs(AVISITN), mode = "last", set_values_to = exprs( AVISITN = 99 ) ) #> # A tibble: 8 × 3 #> USUBJID AVISITN AVAL #> #> 1 1 1 113 #> 2 1 2 111 #> 3 2 1 101 #> 4 2 2 NA #> 5 3 1 NA #> 6 1 99 111 #> 7 2 99 101 #> 8 3 99 NA ``` ### Selecting variables for new records (`keep_source_vars`) Which variables from the source dataset are kept for the new records can be specified by the `keep_source_vars` argument. Variables specified by `by_vars` or `set_values_to` don't need to be added to `keep_source_vars` as these are always kept. ``` adlb <- tribble( ~USUBJID, ~AVISIT, ~AVAL, ~LBSEQ, "1", "WEEK 1", 123, 1, "1", "WEEK 2", 101, 2, "2", "WEEK 1", 99, 1, "2", "WEEK 2", 110, 2, "2", "WEEK 3", 93, 3 ) derive_extreme_records( dataset_add = adlb, filter_add = !is.na(AVAL), by_vars = exprs(USUBJID), order = exprs(AVAL), mode = "first", keep_source_vars = exprs(AVAL), set_values_to = exprs( AVISIT = "MINIMUM" ) ) #> # A tibble: 2 × 3 #> USUBJID AVISIT AVAL #> #> 1 1 MINIMUM 101 #> 2 2 MINIMUM 93 ``` ### Handling duplicates (`check_type`) The source records are checked regarding duplicates with respect to `by_vars` and `order`. By default, a warning is issued if any duplicates are found. ``` adlb <- tribble( ~USUBJID, ~AVISIT, ~AVAL, "1", "WEEK 1", 123, "1", "WEEK 2", 123, "2", "WEEK 1", 99, "2", "WEEK 2", 110, "2", "WEEK 3", 93, ) derive_extreme_records( dataset_add = adlb, filter_add = !is.na(AVAL), by_vars = exprs(USUBJID), order = exprs(AVAL), mode = "first", set_values_to = exprs( AVISIT = "MINIMUM" ) ) #> # A tibble: 2 × 3 #> USUBJID AVISIT AVAL #> #> 1 1 MINIMUM 123 #> 2 2 MINIMUM 93 #> Warning: Dataset contains duplicate records with respect to `USUBJID` and `AVAL` #> i Run `admiral::get_duplicates_dataset()` to access the duplicate records ``` For investigating the issue, the dataset of the duplicate source records can be obtained by calling `get_duplicates_dataset()`: ``` get_duplicates_dataset() #> Duplicate records with respect to `USUBJID` and `AVAL`. #> # A tibble: 2 × 3 #> USUBJID AVAL AVISIT #> * #> 1 1 123 WEEK 1 #> 2 1 123 WEEK 2 ``` Common options to solve the issue are: * Restricting the source records by specifying/updating the `filter_add` argument. * Specifying additional variables for `order`. * Setting `check_type = "none"` to ignore any duplicates. In this example it doesn't matter which of the records with the minimum value is chosen because it doesn't affect the output dataset. Thus the third option is used: ``` derive_extreme_records( dataset_add = adlb, filter_add = !is.na(AVAL), by_vars = exprs(USUBJID), order = exprs(AVAL), mode = "first", check_type = "none", set_values_to = exprs( AVISIT = "MINIMUM" ) ) #> # A tibble: 2 × 3 #> USUBJID AVISIT AVAL #> #> 1 1 MINIMUM 123 #> 2 2 MINIMUM 93 ``` ### Flagging existence of source records (`exist_flag`, `true_value`, `false_value`) If the existence of a source record should be flagged, the `exist_flag` argument can be specified. The specified variable is set to `true_value` if a source record exists. Otherwise, it is set to `false_value`. The `dataset_ref` argument should be specified as otherwise **all** new records originate from `dataset_add`, i.e., `exist_flag` would be set to `true_value` for all records. ``` adsl <- tribble( ~USUBJID, ~DTHDT, "1", ymd("2022-05-13"), "2", ymd(""), "3", ymd("") ) derive_extreme_records( dataset_ref = adsl, dataset_add = adsl, by_vars = exprs(USUBJID), filter_add = !is.na(DTHDT), exist_flag = AVALC, true_value = "Y", false_value = "N", set_values_to = exprs( PARAMCD = "DEATH", ADT = DTHDT ) ) #> # A tibble: 3 × 5 #> USUBJID PARAMCD ADT DTHDT AVALC #> #> 1 1 DEATH 2022-05-13 2022-05-13 Y #> 2 2 DEATH NA NA N #> 3 3 DEATH NA NA N ``` ### Derive `DTYPE = "LOV"` For each subject and parameter the last valid assessment (with respect to `AVISITN` and `LBSEQ`) should be selected and added as a new record to the input dataset. For the new records set `AVISIT = "PBL LAST"`, `AVISITN = 99`, and `DTYPE = "LOV"`. ``` adlb <- tribble( ~USUBJID, ~AVISIT, ~AVISITN, ~PARAMCD, ~AVAL, ~LBSEQ, "1", "BASELINE", 1, "ABC", 120, 1, "1", "WEEK 1", 2, "ABC", 113, 2, "1", "WEEK 1", 2, "ABC", 117, 3, "2", "BASELINE", 1, "ABC", 101, 1, "2", "WEEK 1", 2, "ABC", 101, 2, "2", "WEEK 2", 3, "ABC", 95, 3, "1", "BASELINE", 1, "DEF", 17, 1, "1", "WEEK 1", 2, "DEF", NA, 2, "1", "WEEK 1", 2, "DEF", 13, 3, "2", "BASELINE", 1, "DEF", 9, 1, "2", "WEEK 1", 2, "DEF", 10, 2, "2", "WEEK 2", 3, "DEF", 12, 3 ) %>% mutate(STUDYID = "XYZ", .before = USUBJID) derive_extreme_records( adlb, dataset_add = adlb, filter_add = !is.na(AVAL) & AVISIT != "BASELINE", by_vars = exprs(!!!get_admiral_option("subject_keys"), PARAMCD), order = exprs(AVISITN, LBSEQ), mode = "last", set_values_to = exprs( AVISIT = "PBL LAST", AVISITN = 99, DTYPE = "LOV" ) ) #> # A tibble: 16 × 8 #> STUDYID USUBJID AVISIT AVISITN PARAMCD AVAL LBSEQ DTYPE #> #> 1 XYZ 1 BASELINE 1 ABC 120 1 #> 2 XYZ 1 WEEK 1 2 ABC 113 2 #> 3 XYZ 1 WEEK 1 2 ABC 117 3 #> 4 XYZ 2 BASELINE 1 ABC 101 1 #> 5 XYZ 2 WEEK 1 2 ABC 101 2 #> 6 XYZ 2 WEEK 2 3 ABC 95 3 #> 7 XYZ 1 BASELINE 1 DEF 17 1 #> 8 XYZ 1 WEEK 1 2 DEF NA 2 #> 9 XYZ 1 WEEK 1 2 DEF 13 3 #> 10 XYZ 2 BASELINE 1 DEF 9 1 #> 11 XYZ 2 WEEK 1 2 DEF 10 2 #> 12 XYZ 2 WEEK 2 3 DEF 12 3 #> 13 XYZ 1 PBL LAST 99 ABC 117 3 LOV #> 14 XYZ 1 PBL LAST 99 DEF 13 3 LOV #> 15 XYZ 2 PBL LAST 99 ABC 95 3 LOV #> 16 XYZ 2 PBL LAST 99 DEF 12 3 LOV ``` ### Derive `DTYPE = "MINIMUM"` For each subject and parameter the record with the minimum analysis value should be selected and added as a new record to the input dataset. If there are multiple records meeting the minimum, the first record with respect to `AVISIT` and `LBSEQ` should be selected. For the new records set `AVISIT = "PBL MIN"`, `AVISITN = 97`, and `DTYPE = "MINIMUM"`. ``` derive_extreme_records( adlb, dataset_add = adlb, filter_add = !is.na(AVAL) & AVISIT != "BASELINE", by_vars = exprs(!!!get_admiral_option("subject_keys"), PARAMCD), order = exprs(AVAL, AVISITN, LBSEQ), mode = "first", set_values_to = exprs( AVISIT = "PBL MIN", AVISITN = 97, DTYPE = "MINIMUM" ) ) #> # A tibble: 16 × 8 #> STUDYID USUBJID AVISIT AVISITN PARAMCD AVAL LBSEQ DTYPE #> #> 1 XYZ 1 BASELINE 1 ABC 120 1 #> 2 XYZ 1 WEEK 1 2 ABC 113 2 #> 3 XYZ 1 WEEK 1 2 ABC 117 3 #> 4 XYZ 2 BASELINE 1 ABC 101 1 #> 5 XYZ 2 WEEK 1 2 ABC 101 2 #> 6 XYZ 2 WEEK 2 3 ABC 95 3 #> 7 XYZ 1 BASELINE 1 DEF 17 1 #> 8 XYZ 1 WEEK 1 2 DEF NA 2 #> 9 XYZ 1 WEEK 1 2 DEF 13 3 #> 10 XYZ 2 BASELINE 1 DEF 9 1 #> 11 XYZ 2 WEEK 1 2 DEF 10 2 #> 12 XYZ 2 WEEK 2 3 DEF 12 3 #> 13 XYZ 1 PBL MIN 97 ABC 113 2 MINIMUM #> 14 XYZ 1 PBL MIN 97 DEF 13 3 MINIMUM #> 15 XYZ 2 PBL MIN 97 ABC 95 3 MINIMUM #> 16 XYZ 2 PBL MIN 97 DEF 10 2 MINIMUM ``` ### Derive `DTYPE = "MAXIMUM"` For each subject and parameter the record with the maximum analysis value should be selected and added as a new record to the input dataset. If there are multiple records meeting the maximum, the first record with respect to `AVISIT` and `LBSEQ` should be selected. For the new records set `AVISIT = "PBL MAX"`, `AVISITN = 98`, and `DTYPE = "MAXIMUM"`. ``` derive_extreme_records( adlb, dataset_add = adlb, filter_add = !is.na(AVAL) & AVISIT != "BASELINE", by_vars = exprs(!!!get_admiral_option("subject_keys"), PARAMCD), order = exprs(desc(AVAL), AVISITN, LBSEQ), mode = "first", set_values_to = exprs( AVISIT = "PBL MAX", AVISITN = 99, DTYPE = "MAXIMUM" ) ) #> # A tibble: 16 × 8 #> STUDYID USUBJID AVISIT AVISITN PARAMCD AVAL LBSEQ DTYPE #> #> 1 XYZ 1 BASELINE 1 ABC 120 1 #> 2 XYZ 1 WEEK 1 2 ABC 113 2 #> 3 XYZ 1 WEEK 1 2 ABC 117 3 #> 4 XYZ 2 BASELINE 1 ABC 101 1 #> 5 XYZ 2 WEEK 1 2 ABC 101 2 #> 6 XYZ 2 WEEK 2 3 ABC 95 3 #> 7 XYZ 1 BASELINE 1 DEF 17 1 #> 8 XYZ 1 WEEK 1 2 DEF NA 2 #> 9 XYZ 1 WEEK 1 2 DEF 13 3 #> 10 XYZ 2 BASELINE 1 DEF 9 1 #> 11 XYZ 2 WEEK 1 2 DEF 10 2 #> 12 XYZ 2 WEEK 2 3 DEF 12 3 #> 13 XYZ 1 PBL MAX 99 ABC 117 3 MAXIMUM #> 14 XYZ 1 PBL MAX 99 DEF 13 3 MAXIMUM #> 15 XYZ 2 PBL MAX 99 ABC 101 2 MAXIMUM #> 16 XYZ 2 PBL MAX 99 DEF 12 3 MAXIMUM ``` ### Derive `DTYPE = "WOC"` or `DTYPE = "BOC"` For each subject and parameter the record with the worst analysis value should be selected and added as a new record to the input dataset. The worst value is either the minimum or maximum value depending on the parameter. If there are multiple records meeting the worst value, the first record with respect to `AVISIT` and `LBSEQ` should be selected. For the new records set `AVISIT = "PBL WORST"`, `AVISITN = 96`, and `DTYPE = "WOC"`. Here the maximum is considered worst for `PARAMCD = "ABC"` and the minimum for `PARAMCD = "DEF"`. ``` derive_extreme_records( adlb, dataset_add = adlb, filter_add = !is.na(AVAL) & AVISIT != "BASELINE", by_vars = exprs(!!!get_admiral_option("subject_keys"), PARAMCD), order = exprs( if_else(PARAMCD == "ABC", desc(AVAL), AVAL), AVISITN, LBSEQ ), mode = "first", set_values_to = exprs( AVISIT = "PBL WORST", AVISITN = 96, DTYPE = "WOC" ) ) #> # A tibble: 16 × 8 #> STUDYID USUBJID AVISIT AVISITN PARAMCD AVAL LBSEQ DTYPE #> #> 1 XYZ 1 BASELINE 1 ABC 120 1 #> 2 XYZ 1 WEEK 1 2 ABC 113 2 #> 3 XYZ 1 WEEK 1 2 ABC 117 3 #> 4 XYZ 2 BASELINE 1 ABC 101 1 #> 5 XYZ 2 WEEK 1 2 ABC 101 2 #> 6 XYZ 2 WEEK 2 3 ABC 95 3 #> 7 XYZ 1 BASELINE 1 DEF 17 1 #> 8 XYZ 1 WEEK 1 2 DEF NA 2 #> 9 XYZ 1 WEEK 1 2 DEF 13 3 #> 10 XYZ 2 BASELINE 1 DEF 9 1 #> 11 XYZ 2 WEEK 1 2 DEF 10 2 #> 12 XYZ 2 WEEK 2 3 DEF 12 3 #> 13 XYZ 1 PBL WORST 96 ABC 117 3 WOC #> 14 XYZ 1 PBL WORST 96 DEF 13 3 WOC #> 15 XYZ 2 PBL WORST 96 ABC 101 2 WOC #> 16 XYZ 2 PBL WORST 96 DEF 10 2 WOC ``` ### Derive a parameter for the first disease progression (PD) For each subject in the `ADSL` dataset a new parameter should be added to the input dataset which indicates whether disease progression (PD) occurred (set `AVALC = "Y"`, `AVAL = 1`) or not (set `AVALC = "N"`, `AVAL = 0`). For the new parameter set `PARAMCD = "PD"` and `PARAM = "Disease Progression"`. ``` adsl <- tribble( ~USUBJID, ~DTHDT, "1", ymd("2022-05-13"), "2", ymd(""), "3", ymd("") ) %>% mutate(STUDYID = "XX1234") adrs <- tribble( ~USUBJID, ~RSDTC, ~AVALC, ~AVAL, "1", "2020-01-02", "PR", 2, "1", "2020-02-01", "CR", 1, "1", "2020-03-01", "CR", 1, "2", "2021-06-15", "SD", 3, "2", "2021-07-16", "PD", 4, "2", "2021-09-14", "PD", 4 ) %>% mutate( STUDYID = "XX1234", .before = USUBJID ) %>% mutate( ADT = ymd(RSDTC), PARAMCD = "OVR", PARAM = "Overall Response", .after = RSDTC ) derive_extreme_records( adrs, dataset_ref = adsl, dataset_add = adrs, by_vars = get_admiral_option("subject_keys"), filter_add = PARAMCD == "OVR" & AVALC == "PD", order = exprs(ADT), exist_flag = AVALC, true_value = "Y", false_value = "N", mode = "first", set_values_to = exprs( PARAMCD = "PD", PARAM = "Disease Progression", AVAL = yn_to_numeric(AVALC), ) ) #> # A tibble: 9 × 8 #> STUDYID USUBJID RSDTC ADT PARAMCD PARAM AVALC AVAL #> #> 1 XX1234 1 2020-01-02 2020-01-02 OVR Overall Response PR 2 #> 2 XX1234 1 2020-02-01 2020-02-01 OVR Overall Response CR 1 #> 3 XX1234 1 2020-03-01 2020-03-01 OVR Overall Response CR 1 #> 4 XX1234 2 2021-06-15 2021-06-15 OVR Overall Response SD 3 #> 5 XX1234 2 2021-07-16 2021-07-16 OVR Overall Response PD 4 #> 6 XX1234 2 2021-09-14 2021-09-14 OVR Overall Response PD 4 #> 7 XX1234 2 2021-07-16 2021-07-16 PD Disease Progression Y 1 #> 8 XX1234 1 NA PD Disease Progression N 0 #> 9 XX1234 3 NA PD Disease Progression N 0 ``` ### Derive parameter indicating death For each subject in the `ADSL` dataset a new parameter should be created which indicates whether the subject died (set `AVALC = "Y"`, `AVAL = 1`) or not (set `AVALC = "N"`, `AVAL = 0`). For the new parameter set `PARAMCD = "DEATH"`, `PARAM = "Death"`, and `ADT` to the date of death (`DTHDT`). ``` derive_extreme_records( dataset_ref = adsl, dataset_add = adsl, by_vars = exprs(STUDYID, USUBJID), filter_add = !is.na(DTHDT), exist_flag = AVALC, true_value = "Y", false_value = "N", mode = "first", keep_source_vars = exprs(AVALC), set_values_to = exprs( PARAMCD = "DEATH", PARAM = "Death", ADT = DTHDT ) ) #> # A tibble: 3 × 6 #> STUDYID USUBJID PARAMCD PARAM ADT AVALC #> #> 1 XX1234 1 DEATH Death 2022-05-13 Y #> 2 XX1234 2 DEATH Death NA N #> 3 XX1234 3 DEATH Death NA N ``` The `keep_source_vars` argument is specified to avoid that all `ADSL` variables (like `DTHDT`) are copied to the parameter.

derive_extreme_records function