admiralonco (version 1.4.0)

derive_param_confirmed_bor: Adds a Parameter for Confirmed Best Overall Response

Description

The derive_param_confirmed_bor() function has been superseded in favor of derive_extreme_event().

Adds a parameter for confirmed best overall response (BOR)

Usage

derive_param_confirmed_bor(
  dataset,
  dataset_adsl,
  filter_source,
  source_pd = NULL,
  source_datasets = NULL,
  reference_date,
  ref_start_window,
  ref_confirm,
  max_nr_ne = 1,
  accept_sd = FALSE,
  missing_as_ne = FALSE,
  aval_fun,
  set_values_to,
  subject_keys = get_admiral_option("subject_keys")
)

Value

The input dataset with a new parameter for confirmed best overall response

Arguments

dataset

Input dataset

The PARAMCD, ADT, and AVALC variables and the variables specified by subject_keys and reference_date 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_keys are expected. For each subject of the specified dataset a new observation is added to the input dataset.

filter_source

Source filter

All observations in dataset_source fulfilling the specified condition are considered for deriving the confirmed 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 admiral::date_source() for details)

source_datasets

Source dataset for the first PD date

A named list of datasets is expected. It links the dataset_name from source_pd with an existing dataset.

For example if source_pd = pd_date with

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

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

reference_date

Reference date

The reference date is used for the derivation of "SD" and "NON-CR/NON-PD" response (see "Details" section). Usually it is treatment start date (TRTSDT) or randomization date (RANDDT).

Permitted Values: a numeric date variable

ref_start_window

Stable disease time window

Assessments at least the specified number of days after the reference date (i.e. where ADT >= reference_date + ref_start_window) with response "CR", "PR", "SD", or "NON-CR/NON-PD" are considered for "SD" or "NON-CR/NON-PD" response.

Permitted Values: a non-negative numeric scalar

ref_confirm

Minimum time period for confirmation

The assessment and the confirmatory assessment for "CR" and "PR" have to be at least the specified number of days apart.

max_nr_ne

The specified number of "NE" assessments between the assessment and the confirmatory assessment for "CR" and "PR" response is accepted.

Permitted Values: a non-negative numeric scalar

accept_sd

Accept "SD" for "PR"?

If the argument is set to TRUE, one "SD" assessment between the assessment and the confirmatory assessment for "PR" response is accepted. Otherwise, no "SD" assessment must occur between the two assessments.

Permitted Values: a logical scalar

missing_as_ne

Consider no assessments as "NE"?

If the argument is set to TRUE, the response is set to "NE" for subjects without an assessment in the input dataset. Otherwise, the response is set to "MISSING" for these subjects.

Permitted Values: a logical scalar

aval_fun

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

Variables to set

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

subject_keys

Variables to uniquely identify a subject

A list of symbols created using exprs() is expected.

Author

Stefan Bundfuss

Details

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. The following potential confirmed responses are selected from the restricted input dataset:

   • "CR": An assessment is considered as complete response (CR) if

     • AVALC == "CR",

     • there is a confirmatory assessment with AVALC == "CR" at least ref_confirm days after the assessment,

     • all assessments between the assessment and the confirmatory assessment are "CR" or "NE", and

     • there are at most max_nr_ne "NE" assessments between the assessment and the confirmatory assessment.

   • "PR": An assessment is considered as partial response (PR) if

     • AVALC == "PR",

     • there is a confirmatory assessment with AVALC %in% c("CR", "PR") at least ref_confirm days after the assessment,

     • all assessments between the assessment and the confirmatory assessment are "CR", "PR", "SD", or "NE",

     • there is no "PR" assessment after a "CR" assessment in the confirmation period,

     • there are at most max_nr_ne "NE" assessments between the assessment and the confirmatory assessment, and

     • if the accept_sd argument is set to TRUE, one "SD" assessment in the confirmation period is accepted. Otherwise, no "SD" assessment must occur within the confirmation period.

   • "SD": An assessment is considered as stable disease (SD) if

     • AVALC %in% c("CR", "PR", "SD") and

     • the assessment is at least ref_start_window days after reference_date.

   • "NON-CR/NON-PD": An assessment is considered as NON-CR/NON-PD if

     • AVALC = "NON-CR/NON-PD" and

     • the assessment is at least ref_start_window days after reference_date.

   • "PD": An assessment is considered as progressive disease (PD) if AVALC == "PD".

   • "NE": An assessment is considered as not estimable (NE) if

     • AVALC == "NE" or

     • AVALC %in% c("CR", "PR", "SD", "NON-CR/NON-PD") and the assessment is less than ref_start_window days after reference_date.

   • "ND": An assessment is considered as not done (ND) if AVALC == "ND".

   • "MISSING": An assessment is considered as missing (MISSING) if a subject has no observation in the input dataset.

     If the missing_as_ne argument is set to TRUE, AVALC is set to "NE" for these subjects.

3. For each subject the best response as derived in the previous step is selected, where "CR" is best and "MISSING" is worst in the order above. If the best response is not unique, the first one (with respect to ADT) is selected. If the selected record is from the input dataset, all variables are kept. If the selected record is from dataset_adsl, all variables which are in both dataset and dataset_adsl are kept.

4. The variables specified by the set_values_to parameter are added to the new observations.

5. The new observations are added to input dataset.

See Also

Other deprecated: date_source(), derive_param_bor(), derive_param_clinbenefit(), derive_param_confirmed_resp(), derive_param_response(), filter_pd()

Examples

library(dplyr)
library(lubridate)
library(admiral)
# ensure that `date_source()` from admiralonco is used to avoid deprecation
# warning
unloadNamespace("admiralonco")
library(admiralonco)

# Create ADSL dataset
adsl <- tibble::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",
  "9",      "2020-03-01"
) %>%
  mutate(
    TRTSDT = ymd(TRTSDTC),
    STUDYID = "XX1234"
  )

# Create ADRS dataset
ovr_obs <- tibble::tribble(
  ~USUBJID, ~ADTC,        ~AVALC,
  "1",      "2020-01-01", "PR",
  "1",      "2020-02-01", "CR",
  "1",      "2020-02-16", "NE",
  "1",      "2020-03-01", "CR",
  "1",      "2020-04-01", "SD",
  "2",      "2020-01-01", "SD",
  "2",      "2020-02-01", "PR",
  "2",      "2020-03-01", "SD",
  "2",      "2020-03-13", "CR",
  "3",      "2019-11-12", "CR",
  "3",      "2019-12-02", "CR",
  "3",      "2020-01-01", "SD",
  "4",      "2020-01-01", "PR",
  "4",      "2020-03-01", "SD",
  "4",      "2020-04-01", "SD",
  "4",      "2020-05-01", "PR",
  "4",      "2020-05-15", "NON-CR/NON-PD",
  "5",      "2020-01-01", "PR",
  "5",      "2020-01-10", "SD",
  "5",      "2020-01-20", "PR",
  "5",      "2020-05-15", "NON-CR/NON-PD",
  "6",      "2020-02-06", "PR",
  "6",      "2020-02-16", "CR",
  "6",      "2020-03-30", "PR",
  "6",      "2020-04-12", "PD",
  "6",      "2020-05-01", "CR",
  "6",      "2020-06-01", "CR",
  "7",      "2020-02-06", "PR",
  "7",      "2020-02-16", "CR",
  "7",      "2020-04-01", "NE",
  "9",      "2020-03-16", "CR",
  "9",      "2020-04-01", "NE",
  "9",      "2020-04-16", "NE",
  "9",      "2020-05-01", "CR"
) %>%
  mutate(PARAMCD = "OVR", ANL01FL = "Y")

pd_obs <-
  bind_rows(tibble::tribble(
    ~USUBJID, ~ADTC,        ~AVALC,
    "6",      "2020-04-12", "Y"
  ) %>%
    mutate(PARAMCD = "PD", ANL01FL = "Y"))

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" & ANL01FL == "Y"
)

# Derive confirmed best overall response parameter
derive_param_confirmed_bor(
  adrs,
  dataset_adsl = adsl,
  filter_source = PARAMCD == "OVR" & ANL01FL == "Y",
  source_pd = pd_date,
  source_datasets = list(adrs = adrs),
  reference_date = TRTSDT,
  ref_start_window = 28,
  ref_confirm = 28,
  set_values_to = exprs(
    PARAMCD = "CBOR",
    PARAM = "Best Confirmed Overall Response by Investigator"
  )
) %>%
  filter(PARAMCD == "CBOR")

# Derive confirmed best overall response parameter (accepting SD for PR,
# accept two NEs, and considering missings as NE)
derive_param_confirmed_bor(
  adrs,
  dataset_adsl = adsl,
  filter_source = PARAMCD == "OVR" & ANL01FL == "Y",
  source_pd = pd_date,
  source_datasets = list(adrs = adrs),
  reference_date = TRTSDT,
  ref_start_window = 28,
  ref_confirm = 28,
  max_nr_ne = 2,
  accept_sd = TRUE,
  missing_as_ne = TRUE,
  set_values_to = exprs(
    PARAMCD = "CBOR",
    PARAM = "Best Confirmed Overall Response by Investigator"
  )
) %>%
  filter(PARAMCD == "CBOR")