derive_param_tte {admiral} | R Documentation |
Derive a Time-to-Event Parameter
Description
Add a time-to-event parameter to the input dataset.
Usage
derive_param_tte(
dataset = NULL,
dataset_adsl,
source_datasets,
by_vars = NULL,
start_date = TRTSDT,
event_conditions,
censor_conditions,
create_datetime = FALSE,
set_values_to,
subject_keys = get_admiral_option("subject_keys")
)
Arguments
dataset |
Input dataset
|
dataset_adsl |
ADSL input dataset The variables specified for |
source_datasets |
Source datasets A named list of datasets is expected. The |
by_vars |
By variables If the parameter is specified, separate time to event parameters are derived for each by group. The by variables must be in at least one of the source datasets. Each source dataset must contain either all by variables or none of the by variables. The by variables are not included in the output dataset. Permitted Values: list of variables created by |
start_date |
Time to event origin date The variable If the event or censoring date is before the origin date, |
event_conditions |
Sources and conditions defining events A list of |
censor_conditions |
Sources and conditions defining censorings A list of |
create_datetime |
Create datetime variables? If set to |
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 |
Details
The following steps are performed to create the observations of the new parameter:
Deriving the events:
For each event source dataset the observations as specified by the
filter
element are selected. Then for each patient the first observation (with respect todate
) is selected.The
ADT
variable is set to the variable specified by thedate
element. If the date variable is a datetime variable, only the datepart is copied.The
CNSR
variable is added and set to thecensor
element.The variables specified by the
set_values_to
element are added.The selected observations of all event source datasets are combined into a single dataset.
For each patient the first observation (with respect to the
ADT
variable) from the single dataset is selected.
Deriving the censoring observations:
For each censoring source dataset the observations as specified by the
filter
element are selected. Then for each patient the last observation (with respect todate
) is selected.The
ADT
variable is set to the variable specified by thedate
element. If the date variable is a datetime variable, only the datepart is copied.The
CNSR
variable is added and set to thecensor
element.The variables specified by the
set_values_to
element are added.The selected observations of all censoring source datasets are combined into a single dataset.
For each patient the last observation (with respect to the
ADT
variable) from the single dataset is selected.
For each subject (as defined by the subject_keys
parameter) an
observation is selected. If an event is available, the event observation is
selected. Otherwise the censoring observation is selected.
Finally:
The variable specified for
start_date
is joined from the ADSL dataset. Only subjects in both datasets are kept, i.e., subjects with both an event or censoring and an observation indataset_adsl
.The variables as defined by the
set_values_to
parameter are added.The
ADT
/ADTM
variable is set to the maximum ofADT
/ADTM
andSTARTDT
/STARTDTM
(depending on thecreate_datetime
parameter).The new observations are added to the output dataset.
Value
The input dataset with the new parameter added
See Also
event_source()
, censor_source()
Examples
library(tibble)
library(dplyr, warn.conflicts = FALSE)
library(lubridate)
data("admiral_adsl")
adsl <- admiral_adsl
# derive overall survival parameter
death <- event_source(
dataset_name = "adsl",
filter = DTHFL == "Y",
date = DTHDT,
set_values_to = exprs(
EVNTDESC = "DEATH",
SRCDOM = "ADSL",
SRCVAR = "DTHDT"
)
)
last_alive_dt <- censor_source(
dataset_name = "adsl",
date = LSTALVDT,
set_values_to = exprs(
EVNTDESC = "LAST DATE KNOWN ALIVE",
SRCDOM = "ADSL",
SRCVAR = "LSTALVDT"
)
)
derive_param_tte(
dataset_adsl = adsl,
event_conditions = list(death),
censor_conditions = list(last_alive_dt),
source_datasets = list(adsl = adsl),
set_values_to = exprs(
PARAMCD = "OS",
PARAM = "Overall Survival"
)
) %>%
select(-STUDYID) %>%
filter(row_number() %in% 20:30)
# derive duration of response
# only observations for subjects in dataset_adsl are created
adsl <- tribble(
~USUBJID, ~DTHFL, ~DTHDT, ~RSPDT,
"01", "Y", ymd("2021-06-12"), ymd("2021-03-04"),
"02", "N", NA, NA,
"03", "Y", ymd("2021-08-21"), NA,
"04", "N", NA, ymd("2021-04-14")
) %>%
mutate(STUDYID = "AB42")
adrs <- tribble(
~USUBJID, ~AVALC, ~ADT, ~ASEQ,
"01", "SD", ymd("2021-01-03"), 1,
"01", "PR", ymd("2021-03-04"), 2,
"01", "PD", ymd("2021-05-05"), 3,
"02", "PD", ymd("2021-02-03"), 1,
"04", "SD", ymd("2021-02-13"), 1,
"04", "PR", ymd("2021-04-14"), 2,
"04", "CR", ymd("2021-05-15"), 3
) %>%
mutate(STUDYID = "AB42", PARAMCD = "OVR")
pd <- event_source(
dataset_name = "adrs",
filter = AVALC == "PD",
date = ADT,
set_values_to = exprs(
EVENTDESC = "PD",
SRCDOM = "ADRS",
SRCVAR = "ADTM",
SRCSEQ = ASEQ
)
)
death <- event_source(
dataset_name = "adsl",
filter = DTHFL == "Y",
date = DTHDT,
set_values_to = exprs(
EVENTDESC = "DEATH",
SRCDOM = "ADSL",
SRCVAR = "DTHDT"
)
)
lastvisit <- censor_source(
dataset_name = "adrs",
date = ADT,
censor = 1,
set_values_to = exprs(
EVENTDESC = "LAST TUMOR ASSESSMENT",
SRCDOM = "ADRS",
SRCVAR = "ADTM",
SRCSEQ = ASEQ
)
)
first_response <- censor_source(
dataset_name = "adsl",
date = RSPDT,
censor = 1,
set_values_to = exprs(
EVENTDESC = "FIRST RESPONSE",
SRCDOM = "ADSL",
SRCVAR = "RSPDT"
)
)
derive_param_tte(
dataset_adsl = filter(adsl, !is.na(RSPDT)),
start_date = RSPDT,
event_conditions = list(pd, death),
censor_conditions = list(lastvisit, first_response),
source_datasets = list(adsl = adsl, adrs = adrs),
set_values_to = exprs(
PARAMCD = "DURRSP",
PARAM = "Duration of Response"
)
)
# derive time to adverse event for each preferred term
adsl <- tribble(
~USUBJID, ~TRTSDT, ~EOSDT,
"01", ymd("2020-12-06"), ymd("2021-03-06"),
"02", ymd("2021-01-16"), ymd("2021-02-03")
) %>%
mutate(STUDYID = "AB42")
ae <- tribble(
~USUBJID, ~AESTDTC, ~AESEQ, ~AEDECOD,
"01", "2021-01-03T10:56", 1, "Flu",
"01", "2021-03-04", 2, "Cough",
"01", "2021", 3, "Flu"
) %>%
mutate(STUDYID = "AB42")
ae_ext <- derive_vars_dt(
ae,
dtc = AESTDTC,
new_vars_prefix = "AEST",
highest_imputation = "M",
flag_imputation = "none"
)
ttae <- event_source(
dataset_name = "ae",
date = AESTDT,
set_values_to = exprs(
EVNTDESC = "AE",
SRCDOM = "AE",
SRCVAR = "AESTDTC",
SRCSEQ = AESEQ
)
)
eos <- censor_source(
dataset_name = "adsl",
date = EOSDT,
set_values_to = exprs(
EVNTDESC = "END OF STUDY",
SRCDOM = "ADSL",
SRCVAR = "EOSDT"
)
)
derive_param_tte(
dataset_adsl = adsl,
by_vars = exprs(AEDECOD),
start_date = TRTSDT,
event_conditions = list(ttae),
censor_conditions = list(eos),
source_datasets = list(adsl = adsl, ae = ae_ext),
set_values_to = exprs(
PARAMCD = paste0("TTAE", as.numeric(as.factor(AEDECOD))),
PARAM = paste("Time to First", AEDECOD, "Adverse Event"),
PARCAT1 = "TTAE",
PARCAT2 = AEDECOD
)
) %>%
select(USUBJID, STARTDT, PARAMCD, PARAM, ADT, CNSR, SRCSEQ)