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Adds a Parameter for Best Overall Response (without confirmation)

Source: R/derive_param_bor.R
derive_param_bor.Rd

[Superseded] The derive_param_bor() function has been superseded in favor of derive_extreme_event().

Adds a parameter for best overall response, without confirmation, optionally up to first progressive disease

Usage

derive_param_bor(
  dataset,
  dataset_adsl,
  filter_source,
  source_pd = NULL,
  source_datasets = NULL,
  reference_date,
  ref_start_window,
  missing_as_ne = FALSE,
  aval_fun,
  set_values_to,
  subject_keys = get_admiral_option("subject_keys")
)

Arguments

dataset

The input dataframe from which the Best Overall Response will be derived from and added to.

The columns PARAMCD, ADT, and AVALCand the columns specified in subject_keys and reference_date are expected.

After applying filter_source and/or source_pd the column ADT and the columns specified by subject_keys must be a unique key of the dataframe.

Permitted Values: a data.frame() object

dataset_adsl

ADSL input dataset.

The columns specified in the subject_keys argument are expected. For each subject in the passed dataset a new row is added to the input dataset. Columns in dataset_adsl that also appear in dataset will be populated with the appropriate subject-specific value for these new rows.

Permitted Values: a data.frame() object

filter_source

Filter to be applied to dataset to derive the Best Overall Response

source_pd

Date of first progressive disease (PD)

If the parameter is specified, the observations of the input dataset for deriving the new parameter are restricted to observations up to the specified date. Observations at the specified date are included. For subjects without first PD date all observations are take into account.

Permitted Values: a date_source object (see date_source() for details)

source_datasets

Source dataframe to be used to calculate the first PD date

A named list of dataframes is expected (although for BOR) only one dataframe is needed. It links the dataset_name from source_pd with an existing dataframe.

For example if source_pd = pd_date with

pd_date <- date_source(
  dataset_name = "adrs",
  date = ADT,
  filter = PARAMCD == PD
)

and the actual response dataframe in the script is myadrs, source_datasets = list(adrs = myadrs) should be specified.

reference_date

Reference date

The reference date is used along with ref_start_window to determine those records that occur before and after ADT (see Details section for further information). Usually it is treatment start date (TRTSDT) or randomization date (RANDDT).

Permitted Values: a numeric date column

ref_start_window

Stable disease time window

The ref_start_window is used along with reference_date to determine those records that occur before and after ADT (i.e. for a record determine whether ADT >= reference_date + ref_start_window), see Details section for further information.

Permitted Values: a non-negative numeric scalar

missing_as_ne

Consider no assessments as "NE"?

If the argument is set to TRUE, the response is set to "NE" for subjects in dataset_adsl without an assessment in the dataset after the filter has been applied. Otherwise, the response is set to "MISSING" for these subjects.

Permitted Values: a logical scalar

aval_fun

Deprecated, please use set_values_to instead.

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.

set_values_to

New columns to set

A named list returned by exprs() defining the columns to be set for the new parameter, e.g. exprs(PARAMCD = "BOR", PARAM = "Best Overall Response") is expected. The values must be symbols, character strings, numeric values, or NA.

subject_keys

Columns to uniquely identify a subject

Permitted Values: A list of symbols created using exprs().

Value

The dataframe passed in the dataset argument with additional columns and/or rows as set in the set_values_to argument.

Details

Calculates the best overall response (BOR) parameter, as detailed below.

Records after PD can be removed using the source_pd and source_datasets arguments.

Note:

  1. All CR, PR and PD response records are considered for Best Overall Response.

  2. All SD or NON-CR/NON-PD records where ADT >= reference_date + ref_start_window are also considered for Best Overall Response.

  3. Subjects with ONLY an SD or NON-CR/NON-PD records where ADT < reference_date + ref_start_window are assigned a Best Overall Response of NE.

  4. The Best Response, from the records in steps 1 to 3, is then selected in the following order of preference: CR, PR, SD, NON-CR/NON-PD, PD, NE, MISSING

  5. The AVAL column is added and set using the aval_fun(AVALC) function

  6. The columns specified by the set_values_to parameter and records are added to the dataframe passed into the dataset argument

Note: Any responses of SD or NON-CR/NON-PD that occur before reference_date + ref_start_window are ignored in the calculation of BOR. All other responses are included in the calculation of BOR, irrespective of the number of days from the reference date.

Also Note: All columns from the input dataset are kept. For subjects with no records in the input dataset (after the filter is applied) all columns are kept from ADSL which are also in the input dataset. Columns which are not to be populated for the new parameter or populated differently (e.g. RSSTRESC, VISIT, PARCATy, ANLzzFL, ...) should be overwritten using the set_values_to parameter.

See also

Other superseded: derive_param_clinbenefit(), derive_param_confirmed_bor(), derive_param_confirmed_resp(), derive_param_response(), filter_pd()

Author

Stephen Gormley

Examples


library(magrittr)
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
library(tibble)
library(lubridate)
#> 
#> Attaching package: ‘lubridate’
#> The following objects are masked from ‘package:base’:
#> 
#>     date, intersect, setdiff, union
library(admiral)
#> 
#> Attaching package: ‘admiral’
#> The following objects are masked from ‘package:admiralonco’:
#> 
#>     death_event, lastalive_censor

# Create ADSL dataset
adsl <- tribble(
  ~USUBJID, ~TRTSDTC,
  "1",      "2020-01-01",
  "2",      "2019-12-12",
  "3",      "2019-11-11",
  "4",      "2019-12-30",
  "5",      "2020-01-01",
  "6",      "2020-02-02",
  "7",      "2020-02-02",
  "8",      "2020-04-01"
) %>%
  mutate(
    TRTSDT = ymd(TRTSDTC),
    STUDYID = "XX1234"
  )

# Create ADRS dataset
ovr_obs <- tribble(
  ~USUBJID, ~ADTC, ~AVALC, ~ANL01FL,
  "1", "2020-01-01", "PR", "Y",
  "1", "2020-02-01", "CR", "Y",
  "1", "2020-02-16", "NE", "Y",
  "1", "2020-03-01", "CR", "Y",
  "1", "2020-04-01", "SD", "Y",
  "2", "2020-01-01", "SD", "Y",
  "2", "2020-02-01", "PR", "Y",
  "2", "2020-03-01", "SD", "Y",
  "2", "2020-03-13", "CR", "Y",
  "3", "2019-11-12", "CR", "Y",
  "3", "2019-12-02", "CR", "Y",
  "3", "2020-01-01", "SD", "Y",
  "4", "2020-01-01", "PR", "Y",
  "4", "2020-03-01", "SD", "N",
  "4", "2020-04-01", "SD", "Y",
  "4", "2020-05-01", "PR", "Y",
  "4", "2020-05-15", "NON-CR/NON-PD", "Y",
  "5", "2020-01-01", "PR", "Y",
  "5", "2020-01-10", "SD", "Y",
  "5", "2020-01-20", "PR", "Y",
  "5", "2020-05-15", "NON-CR/NON-PD", "Y",
  "6", "2020-02-06", "PR", "Y",
  "6", "2020-02-16", "CR", "Y",
  "6", "2020-03-30", "PR", "Y",
  "6", "2020-04-12", "PD", "Y",
  "6", "2020-05-01", "CR", "Y",
  "6", "2020-06-01", "CR", "Y",
  "7", "2020-02-06", "PR", "Y",
  "7", "2020-02-16", "CR", "Y",
  "7", "2020-04-01", "NE", "N"
) %>%
  mutate(PARAMCD = "OVR")

pd_obs <-
  bind_rows(tribble(
    ~USUBJID, ~ADTC,        ~AVALC,
    "2",      "2020-03-01", "Y",
    "4",      "2020-02-01", "Y"
  ) %>%
    mutate(PARAMCD = "PD"))

adrs <- bind_rows(ovr_obs, pd_obs) %>%
  mutate(
    ADT = ymd(ADTC),
    STUDYID = "XX1234"
  ) %>%
  select(-ADTC) %>%
  derive_vars_merged(
    dataset_add = adsl,
    by_vars     = exprs(STUDYID, USUBJID),
    new_vars    = exprs(TRTSDT)
  )

pd_date <- date_source(
  dataset_name = "adrs",
  date         = ADT,
  filter       = PARAMCD == "PD"
)

aval_fun_pass <- function(arg) {
  case_when(
    arg == "CR" ~ 11,
    arg == "PR" ~ 22,
    arg == "SD" ~ 33,
    arg == "NON-CR/NON-PD" ~ 44,
    arg == "PD" ~ 55,
    arg == "NE" ~ 66,
    arg == "MISSING" ~ 77,
    TRUE ~ NA_real_
  )
}

# Derive best overall response parameter
derive_param_bor(
  adrs,
  dataset_adsl = adsl,
  filter_source = PARAMCD == "OVR" & ANL01FL == "Y",
  source_pd = pd_date,
  source_datasets = list(adrs = adrs),
  aval_fun = aval_fun_pass,
  reference_date = TRTSDT,
  ref_start_window = 28,
  set_values_to = exprs(
    PARAMCD = "BOR",
    PARAM = "Best Overall Response"
  )
) %>%
  filter(PARAMCD == "BOR")
#> Warning: The `aval_fun` argument of `derive_param_bor()` is deprecated as of admiralonco
#> 0.4.0.
#> ℹ Please use the `set_values_to` argument instead.
#> # A tibble: 8 × 9
#>   USUBJID AVALC   ANL01FL PARAMCD ADT        STUDYID TRTSDT     PARAM       AVAL
#>   <chr>   <chr>   <chr>   <chr>   <date>     <chr>   <date>     <chr>      <dbl>
#> 1 1       CR      Y       BOR     2020-02-01 XX1234  2020-01-01 Best Over…    11
#> 2 2       PR      Y       BOR     2020-02-01 XX1234  2019-12-12 Best Over…    22
#> 3 3       CR      Y       BOR     2019-11-12 XX1234  2019-11-11 Best Over…    11
#> 4 4       PR      Y       BOR     2020-01-01 XX1234  2019-12-30 Best Over…    22
#> 5 5       PR      Y       BOR     2020-01-01 XX1234  2020-01-01 Best Over…    22
#> 6 6       CR      Y       BOR     2020-02-16 XX1234  2020-02-02 Best Over…    11
#> 7 7       CR      Y       BOR     2020-02-16 XX1234  2020-02-02 Best Over…    11
#> 8 8       MISSING NA      BOR     NA         XX1234  2020-04-01 Best Over…    77