| derive_param_confirmed_bor {admiralonco} | R Documentation |
Adds a parameter for confirmed best overall response (BOR)
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")
)
dataset |
Input dataset The After applying |
dataset_adsl |
ADSL input dataset The variables specified for |
filter_source |
Source filter All observations in |
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 |
source_datasets |
Source dataset for the first PD date A named list of datasets is expected. It links the For example if pd_date <- date_source( dataset_name = "adrs", date = ADT, filter = PARAMCD == PD ) and the actual response dataset in the script is |
reference_date |
Reference date The reference date is used for the derivation of 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 Permitted Values: a non-negative numeric scalar |
ref_confirm |
Minimum time period for confirmation The assessment and the confirmatory assessment for |
max_nr_ne |
The specified number of Permitted Values: a non-negative numeric scalar |
accept_sd |
Accept If the argument is set to Permitted Values: a logical scalar |
missing_as_ne |
Consider no assessments as If the argument is set to Permitted Values: a logical scalar |
aval_fun |
Deprecated, please use Function to map character analysis value ( 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 |
subject_keys |
Variables to uniquely identify a subject A list of symbols created using |
The input dataset (dataset) is restricted to the observations matching
filter_source and to observations before or at the date specified by
source_pd.
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.
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.
The AVAL variable is added and set to aval_fun(AVALC).
The variables specified by the set_values_to parameter are added to
the new observations.
The new observations are added to input dataset.
The input dataset with a new parameter for confirmed best overall response
Stefan Bundfuss
ADRS Functions for adding Parameters:
derive_param_bor(),
derive_param_clinbenefit(),
derive_param_confirmed_resp(),
derive_param_response()
library(dplyr)
library(lubridate)
library(admiral)
# 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")