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Adds a Parameter for Clinical Benefit

Source: R/derive_param_clinbenefit.R
derive_param_clinbenefit.Rd

Adds a parameter for clinical benefit/disease control

Usage

derive_param_clinbenefit(
  dataset,
  dataset_adsl,
  filter_source,
  source_resp,
  source_pd = NULL,
  source_datasets,
  reference_date,
  ref_start_window,
  aval_fun = yn_to_numeric,
  clinben_vals = c("CR", "PR", "SD", "NON-CR/NON-PD"),
  set_values_to,
  subject_keys = get_admiral_option("subject_keys")
)

Arguments

dataset

Input dataset. This is the dataset to which the clinical benefit rate parameter will be added.

The variables PARAMCD, AVALC, ADT, and those specified by the subject_keys parameter and the reference_date parameter are expected.

After applying filter_source and/or source_pd the variable ADT and the variables specified by subject_keys must be a unique key of the dataset.

dataset_adsl

ADSL input dataset.

The variables specified for subject_keysis expected. For each subject of the specified dataset a new observation is added to the input dataset. Variables in dataset_adsl that also appear in dataset will be populated with the appropriate subject-specific value for these new observations.

filter_source

Filter condition in dataset that represents records for overall disease response assessment for a subject at a given timepoint, e.g. PARAMCD == "OVR" or PARAMCD == "OVRLRESP".

source_resp

A date_source object specifying the dataset, date variable, and filter condition used to identify response status.

source_pd

A date_source object specifying the dataset, date variable, and filter condition used to identify disease progression.

source_datasets

A named list of data sets is expected.

The list must contain the names provided by the dataset_name field of the date_source() objects specified for source_pd and source_resp.

reference_date

Name of variable representing the index date for ref_start_window. A variable providing a date. An unquoted symbol is expected.

ref_start_window

Integer representing number of days from reference_date that must elapse before an evaluable non-PD assessment counts toward determining clinical benefit.

aval_fun

Function to map character analysis value (AVALC) to numeric analysis value (AVAL)

The (first) argument of the function must expect a character vector and the function must return a numeric vector.

clinben_vals

A vector of response values to be considered when determining clinical benefit.

set_values_to

A named list returned by vars() containing new variables and their static value to be populated for the clinical benefit rate parameter records, e.g. vars(PARAMCD = "CBR", PARAM = "Clinical Benefit Rate").

subject_keys

A named list returned by vars() containing variables used to uniquely identify subjects.

Value

The input dataset with a new parameter for clinical benefit

Details

Clinical benefit/disease control is first identified by looking for subjects having response status, and then derived for subjects that have at least one evaluable non-PD response assessment prior to first PD (Progressive Disease) (i.e., responses inclusive of CR, PR, SD, and NON-CR/NON-PD) and after a specified amount of time from a reference date (ref_start_window).

Note: The user input values they wish to include when determining clinical benefit using the argument clinben_vals. The default values for this are CR, PR, SD, and NON-CR/NON-PD, as listed above. In the below example, eligible values be limited to CR and PR.

Example: clinben_vals <- c("CR", "PR")

  1. The input dataset (dataset) is restricted to the observations matching filter_source and to observations before or at the date specified by source_pd.

  2. This dataset is further restricted to include user-generated response assessments from clinben_vals or include response assessments of CR, PR, SD, and NON-CR/NON-PD, exclude missing response assessments, and exclude those less than ref_start_window after reference_date. The earliest assessment by ADT is then selected.

  3. The dataset identified by dataset in source_resp is restricted according to its filter argument. The variable corresponding to the date parameter of source_resp is considered together with ADT from the previous step.

  4. For the observations being added to dataset, ADT is set to the earlier of the first assessment date representing an evaluable non-PD assessment prior to first PD, or the date representing the start of response.

  5. For the observations being added to dataset, AVALC is set to

    • Y for those subjects in the dataset meeting the criteria for clinical benefit above

    • N for subjects not meeting the clinical benefit criteria in dataset or the dataset identified in source_resp

    • N for subjects present in dataset_adsl but not present in dataset or the dataset identified in source_resp.

  6. AVAL is derived using AVALC as input to the function specified in aval_fun.

  7. The variables specified by set_values_to are added to the new observations with values equal to the values specified in the same.

  8. The new observations are added to dataset. Variables held in common between dataset and dataset_adsl are kept for the new observations, and are populated with their values from dataset_adsl.

See also

ADRS Functions for adding Parameters: derive_param_bor(), derive_param_confirmed_bor(), derive_param_confirmed_resp(), derive_param_response()

Author

Andrew Smith

Examples

library(lubridate)
library(dplyr)
library(admiral)

adsl <- tibble::tribble(
  ~USUBJID, ~TRTSDT,
  "01",     ymd("2020-01-14"),
  "02",     ymd("2021-02-16"),
  "03",     ymd("2021-03-09"),
  "04",     ymd("2021-04-21")
) %>%
  mutate(STUDYID = "AB42")

adrs <- tibble::tribble(
  ~USUBJID, ~PARAMCD, ~AVALC, ~ADT,
  "01",     "RSP",    "Y",    ymd("2021-03-14"),
  "02",     "RSP",    "N",    ymd("2021-05-07"),
  "03",     "RSP",    "N",    NA,
  "04",     "RSP",    "N",    NA,
  "01",     "PD",     "N",    NA,
  "02",     "PD",     "Y",    ymd("2021-05-07"),
  "03",     "PD",     "N",    NA,
  "04",     "PD",     "N",    NA,
  "01",     "OVR",    "SD",   ymd("2020-03-14"),
  "01",     "OVR",    "PR",   ymd("2021-04-13"),
  "02",     "OVR",    "PR",   ymd("2021-04-08"),
  "02",     "OVR",    "PD",   ymd("2021-05-07"),
  "02",     "OVR",    "CR",   ymd("2021-06-20"),
  "03",     "OVR",    "SD",   ymd("2021-03-30"),
  "04",     "OVR",    "NE",   ymd("2021-05-21"),
  "04",     "OVR",    "NA",   ymd("2021-06-30"),
  "04",     "OVR",    "NE",   ymd("2021-07-24"),
  "04",     "OVR",    "ND",   ymd("2021-09-04"),
) %>%
  mutate(STUDYID = "AB42", ANL01FL = "Y") %>%
  derive_vars_merged(
    dataset_add = adsl,
    by_vars = vars(STUDYID, USUBJID),
    new_vars = vars(TRTSDT)
  )

pd <- date_source(
  dataset_name = "adrs",
  date = ADT,
  filter = PARAMCD == "PD" & AVALC == "Y" & ANL01FL == "Y"
)

resp <- date_source(
  dataset_name = "adrs",
  date = ADT,
  filter = PARAMCD == "RSP" & AVALC == "Y" & ANL01FL == "Y"
)

derive_param_clinbenefit(
  dataset = adrs,
  dataset_adsl = adsl,
  filter_source = PARAMCD == "OVR" & ANL01FL == "Y",
  source_resp = resp,
  source_pd = pd,
  source_datasets = list(adrs = adrs),
  reference_date = TRTSDT,
  ref_start_window = 28,
  set_values_to = vars(
    PARAMCD = "CBR"
  )
) %>%
  filter(PARAMCD == "CBR")
#> # A tibble: 4 x 8
#>   USUBJID PARAMCD AVALC ADT        STUDYID ANL01FL TRTSDT      AVAL
#>   <chr>   <chr>   <chr> <date>     <chr>   <chr>   <date>     <dbl>
#> 1 01      CBR     Y     2020-03-14 AB42    Y       2020-01-14     1
#> 2 02      CBR     Y     2021-04-08 AB42    Y       2021-02-16     1
#> 3 03      CBR     N     NA         AB42    NA      2021-03-09     0
#> 4 04      CBR     N     NA         AB42    NA      2021-04-21     0