This article describes creating an ADRS
ADaM with
oncology endpoint parameters based on RECIST v1.1. It shows an
alternative way of deriving the endpoints presented in Creating a Basic ADRS and additionally
modified versions of the endpoints (see Derive
Non-standard Parameters) which cannot be derived by the admiralonco
functions. Most of the endpoints are derived by calling
admiral::derive_extreme_event()
. It is very flexible. Thus
the examples in this vignette can also be used as a starting point for
implementing other response criteria than RECIST 1.1, e.g., iRECIST or
International Myeloma Working Group (IMWG) criteria for the diagnosis of
multiple myeloma.
Examples are currently presented and tested using ADSL
(ADaM) and RS
, TU
(SDTM) inputs. However,
other domains could be used. The functions and workflow could similarly
be used to create an intermediary ADEVENT
ADaM.
Note: All examples assume CDISC SDTM and/or ADaM format as input unless otherwise specified.
AVAL
for New ParametersASEQ
To start, all data frames needed for the creation of
ADRS
should be read into the environment. This will be a
company specific process. Some of the data frames needed may be
ADSL
, RS
and TU
.
For example purpose, the SDTM and ADaM datasets (based on CDISC Pilot
test data)—which are included in {pharmaversesdtm}
and
{pharmaverseadam}
—are used.
library(admiral)
library(admiralonco)
library(dplyr)
library(pharmaversesdtm)
library(pharmaverseadam)
library(lubridate)
library(stringr)
data("adsl")
data("rs_onco_recist")
data("tu_onco_recist")
rs <- rs_onco_recist
tu <- tu_onco_recist
rs <- convert_blanks_to_na(rs)
tu <- convert_blanks_to_na(tu)
At this step, it may be useful to join ADSL
to your
RS
domain. Only the ADSL
variables used for
derivations are selected at this step. The rest of the relevant
ADSL
would be added later.
adsl_vars <- exprs(RANDDT)
adrs <- derive_vars_merged(
rs,
dataset_add = adsl,
new_vars = adsl_vars,
by_vars = get_admiral_option("subject_keys")
)
USUBJID | RSTESTCD | RSDTC | VISIT | RANDDT |
---|---|---|---|---|
01-701-1015 | OVRLRESP | 2014-01-23 | WEEK 3 | 2014-01-02 |
01-701-1015 | OVRLRESP | 2014-01-23 | WEEK 3 | 2014-01-02 |
01-701-1015 | OVRLRESP | 2014-01-23 | WEEK 3 | 2014-01-02 |
01-701-1015 | OVRLRESP | 2014-02 | WEEK 6 | 2014-01-02 |
01-701-1015 | OVRLRESP | 2014-02 | WEEK 6 | 2014-01-02 |
01-701-1015 | OVRLRESP | 2014-02 | WEEK 6 | 2014-01-02 |
01-701-1015 | OVRLRESP | 2014-03-06 | WEEK 9 | 2014-01-02 |
01-701-1015 | OVRLRESP | 2014-03-06 | WEEK 9 | 2014-01-02 |
01-701-1015 | OVRLRESP | 2014-03-06 | WEEK 9 | 2014-01-02 |
01-701-1028 | OVRLRESP | 2013-08-09 | WEEK 3 | 2013-07-19 |
The first step involves company-specific pre-processing of records
for the required input to the downstream parameter functions. Note that
this could be needed multiple times (e.g. once for investigator and once
for Independent Review Facility (IRF)/Blinded Independent Central Review
(BICR) records). It could even involve merging input data from other
sources besides RS
, such as ADTR
.
This step would include any required selection/derivation of
ADT
or applying any necessary partial date imputations,
updating AVAL
(e.g. this should be ordered from best to
worst response), and setting analysis flag ANL01FL
. Common
options for ANL01FL
would be to set null for invalid
assessments or those occurring after new anti-cancer therapy, or to only
flag assessments on or after after date of first
treatment/randomization, or rules to cover the case when a patient has
multiple observations per visit (e.g. by selecting worst value). Another
consideration could be extra potential protocol-specific sources of
Progressive Disease such as radiological assessments, which could be
pre-processed here to create a PD record to feed downstream
derivations.
For the derivation of the parameters it is expected that the subject
identifier variables (usually STUDYID
and
USUBJID
) and ADT
are a unique key. This can be
achieved by deriving an analysis flag (ANLzzFL
). See Derive ANL01FL
for an example.
The below shows an example of a possible company-specific implementation of this step.
In this case we use the overall response records from RS
from the investigator as our starting point. The parameter details such
as PARAMCD
, PARAM
etc will always be
company-specific, but an example is shown below so that you can trace
through how these records feed into the other parameter derivations.
adrs <- adrs %>%
filter(RSEVAL == "INVESTIGATOR" & RSTESTCD == "OVRLRESP") %>%
mutate(
PARAMCD = "OVR",
PARAM = "Overall Response by Investigator",
PARCAT1 = "Tumor Response",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1"
)
USUBJID | VISIT | RSTESTCD | RSEVAL | PARAMCD | PARAM | PARCAT1 | PARCAT2 | PARCAT3 |
---|---|---|---|---|---|---|---|---|
01-701-1015 | WEEK 3 | OVRLRESP | INVESTIGATOR | OVR | Overall Response by Investigator | Tumor Response | Investigator | RECIST 1.1 |
01-701-1015 | WEEK 6 | OVRLRESP | INVESTIGATOR | OVR | Overall Response by Investigator | Tumor Response | Investigator | RECIST 1.1 |
01-701-1015 | WEEK 9 | OVRLRESP | INVESTIGATOR | OVR | Overall Response by Investigator | Tumor Response | Investigator | RECIST 1.1 |
01-701-1028 | WEEK 3 | OVRLRESP | INVESTIGATOR | OVR | Overall Response by Investigator | Tumor Response | Investigator | RECIST 1.1 |
01-701-1028 | WEEK 6 | OVRLRESP | INVESTIGATOR | OVR | Overall Response by Investigator | Tumor Response | Investigator | RECIST 1.1 |
01-701-1028 | WEEK 9 | OVRLRESP | INVESTIGATOR | OVR | Overall Response by Investigator | Tumor Response | Investigator | RECIST 1.1 |
01-701-1034 | WEEK 3 | OVRLRESP | INVESTIGATOR | OVR | Overall Response by Investigator | Tumor Response | Investigator | RECIST 1.1 |
01-701-1034 | WEEK 6 | OVRLRESP | INVESTIGATOR | OVR | Overall Response by Investigator | Tumor Response | Investigator | RECIST 1.1 |
01-701-1097 | WEEK 3 | OVRLRESP | INVESTIGATOR | OVR | Overall Response by Investigator | Tumor Response | Investigator | RECIST 1.1 |
01-701-1115 | WEEK 3 | OVRLRESP | INVESTIGATOR | OVR | Overall Response by Investigator | Tumor Response | Investigator | RECIST 1.1 |
ADT
,
ADTF
, AVISIT
etcIf your data collection allows for partial dates, you could apply a
company-specific imputation rule at this stage when deriving
ADT
. For this example, here we impute missing day to last
possible date.
adrs <- adrs %>%
derive_vars_dt(
dtc = RSDTC,
new_vars_prefix = "A",
highest_imputation = "D",
date_imputation = "last"
) %>%
mutate(AVISIT = VISIT)
USUBJID | AVISIT | PARAMCD | PARAM | RSSTRESC | RSDTC | ADT | ADTF |
---|---|---|---|---|---|---|---|
01-701-1015 | WEEK 3 | OVR | Overall Response by Investigator | SD | 2014-01-23 | 2014-01-23 | NA |
01-701-1015 | WEEK 6 | OVR | Overall Response by Investigator | NE | 2014-02 | 2014-02-28 | D |
01-701-1015 | WEEK 9 | OVR | Overall Response by Investigator | CR | 2014-03-06 | 2014-03-06 | NA |
01-701-1028 | WEEK 3 | OVR | Overall Response by Investigator | SD | 2013-08-09 | 2013-08-09 | NA |
01-701-1028 | WEEK 6 | OVR | Overall Response by Investigator | PD | 2013-08-30 | 2013-08-30 | NA |
01-701-1028 | WEEK 9 | OVR | Overall Response by Investigator | SD | 2013-09-20 | 2013-09-20 | NA |
01-701-1034 | WEEK 3 | OVR | Overall Response by Investigator | NON-CR/NON-PD | 2014-07-22 | 2014-07-22 | NA |
01-701-1034 | WEEK 6 | OVR | Overall Response by Investigator | NON-CR/NON-PD | 2014-08-12 | 2014-08-12 | NA |
01-701-1097 | WEEK 3 | OVR | Overall Response by Investigator | NON-CR/NON-PD | 2014-01-22 | 2014-01-22 | NA |
01-701-1115 | WEEK 3 | OVR | Overall Response by Investigator | SD | 2012-12-21 | 2012-12-21 | NA |
AVALC
and AVAL
Here we populate AVALC
and create the numeric version as
AVAL
(ordered from best to worst response). The
AVAL
values are not considered in the parameter derivations
below, and so changing AVAL
here would not change the
result of those derivations.
USUBJID | AVISIT | PARAMCD | PARAM | RSSTRESC | AVALC | AVAL |
---|---|---|---|---|---|---|
01-701-1015 | WEEK 3 | OVR | Overall Response by Investigator | SD | SD | 3 |
01-701-1015 | WEEK 6 | OVR | Overall Response by Investigator | NE | NE | 6 |
01-701-1015 | WEEK 9 | OVR | Overall Response by Investigator | CR | CR | 1 |
01-701-1028 | WEEK 3 | OVR | Overall Response by Investigator | SD | SD | 3 |
01-701-1028 | WEEK 6 | OVR | Overall Response by Investigator | PD | PD | 5 |
01-701-1028 | WEEK 9 | OVR | Overall Response by Investigator | SD | SD | 3 |
01-701-1034 | WEEK 3 | OVR | Overall Response by Investigator | NON-CR/NON-PD | NON-CR/NON-PD | 4 |
01-701-1034 | WEEK 6 | OVR | Overall Response by Investigator | NON-CR/NON-PD | NON-CR/NON-PD | 4 |
01-701-1097 | WEEK 3 | OVR | Overall Response by Investigator | NON-CR/NON-PD | NON-CR/NON-PD | 4 |
01-701-1115 | WEEK 3 | OVR | Overall Response by Investigator | SD | SD | 3 |
ANL01FL
)When deriving ANL01FL
this is an opportunity to exclude
any records that should not contribute to any downstream parameter
derivations. In the below example this includes only selecting valid
assessments and those occurring on or after randomization date. If there
is more than one assessment at a date, the worst one is flagged.
worst_resp <- function(arg) {
case_when(
arg == "NE" ~ 1,
arg == "CR" ~ 2,
arg == "PR" ~ 3,
arg == "SD" ~ 4,
arg == "NON-CR/NON-PD" ~ 5,
arg == "PD" ~ 6,
TRUE ~ 0
)
}
adrs <- adrs %>%
restrict_derivation(
derivation = derive_var_extreme_flag,
args = params(
by_vars = c(get_admiral_option("subject_keys"), exprs(ADT)),
order = exprs(worst_resp(AVALC), RSSEQ),
new_var = ANL01FL,
mode = "last"
),
filter = !is.na(AVAL) & ADT >= RANDDT
)
USUBJID | AVISIT | PARAMCD | PARAM | AVALC | ADT | RANDDT | ANL01FL |
---|---|---|---|---|---|---|---|
01-701-1015 | WEEK 3 | OVR | Overall Response by Investigator | SD | 2014-01-23 | 2014-01-02 | Y |
01-701-1015 | WEEK 6 | OVR | Overall Response by Investigator | NE | 2014-02-28 | 2014-01-02 | Y |
01-701-1015 | WEEK 9 | OVR | Overall Response by Investigator | CR | 2014-03-06 | 2014-01-02 | Y |
01-701-1028 | WEEK 3 | OVR | Overall Response by Investigator | SD | 2013-08-09 | 2013-07-19 | Y |
01-701-1028 | WEEK 6 | OVR | Overall Response by Investigator | PD | 2013-08-30 | 2013-07-19 | Y |
01-701-1028 | WEEK 9 | OVR | Overall Response by Investigator | SD | 2013-09-20 | 2013-07-19 | Y |
01-701-1034 | WEEK 3 | OVR | Overall Response by Investigator | NON-CR/NON-PD | 2014-07-22 | 2014-07-01 | Y |
01-701-1034 | WEEK 6 | OVR | Overall Response by Investigator | NON-CR/NON-PD | 2014-08-12 | 2014-07-01 | Y |
01-701-1097 | WEEK 3 | OVR | Overall Response by Investigator | NON-CR/NON-PD | 2014-01-22 | 2014-01-01 | Y |
01-701-1115 | WEEK 3 | OVR | Overall Response by Investigator | SD | 2012-12-21 | 2012-11-30 | Y |
Here is an alternative example where those records occurring after
new anti-cancer therapy are additionally excluded (where
NACTDT
would be pre-derived as first date of new
anti-cancer therapy. See {admiralonco}
Creating and Using New Anti-Cancer Start Date for
deriving this variable).
ANL02FL
)To restrict response data up to and including first reported
progressive disease ANL02FL
flag could be created by using
{admiral}
function
admiral::derive_var_relative_flag()
.
adrs <- adrs %>%
derive_var_relative_flag(
by_vars = get_admiral_option("subject_keys"),
order = exprs(ADT, RSSEQ),
new_var = ANL02FL,
condition = AVALC == "PD",
mode = "first",
selection = "before",
inclusive = TRUE
)
USUBJID | AVISIT | PARAMCD | AVALC | ADT | ANL01FL | ANL02FL |
---|---|---|---|---|---|---|
01-701-1015 | WEEK 3 | OVR | SD | 2014-01-23 | Y | Y |
01-701-1015 | WEEK 6 | OVR | NE | 2014-02-28 | Y | Y |
01-701-1015 | WEEK 9 | OVR | CR | 2014-03-06 | Y | Y |
01-701-1028 | WEEK 3 | OVR | SD | 2013-08-09 | Y | Y |
01-701-1028 | WEEK 6 | OVR | PD | 2013-08-30 | Y | Y |
01-701-1028 | WEEK 9 | OVR | SD | 2013-09-20 | Y | NA |
01-701-1034 | WEEK 3 | OVR | NON-CR/NON-PD | 2014-07-22 | Y | Y |
01-701-1034 | WEEK 6 | OVR | NON-CR/NON-PD | 2014-08-12 | Y | Y |
01-701-1097 | WEEK 3 | OVR | NON-CR/NON-PD | 2014-01-22 | Y | Y |
01-701-1115 | WEEK 3 | OVR | SD | 2012-12-21 | Y | Y |
For most parameter derivations the post-baseline overall response assessments up to and including first PD are considered.
USUBJID | AVISIT | AVALC | ADT | RANDDT |
---|---|---|---|---|
01-701-1015 | WEEK 3 | SD | 2014-01-23 | 2014-01-02 |
01-701-1015 | WEEK 6 | NE | 2014-02-28 | 2014-01-02 |
01-701-1015 | WEEK 9 | CR | 2014-03-06 | 2014-01-02 |
01-701-1028 | WEEK 3 | SD | 2013-08-09 | 2013-07-19 |
01-701-1028 | WEEK 6 | PD | 2013-08-30 | 2013-07-19 |
01-701-1034 | WEEK 3 | NON-CR/NON-PD | 2014-07-22 | 2014-07-01 |
01-701-1034 | WEEK 6 | NON-CR/NON-PD | 2014-08-12 | 2014-07-01 |
01-701-1097 | WEEK 3 | NON-CR/NON-PD | 2014-01-22 | 2014-01-01 |
01-701-1115 | WEEK 3 | SD | 2012-12-21 | 2012-11-30 |
01-701-1115 | WEEK 6 | PR | 2013-01-11 | 2012-11-30 |
The building blocks for the events that contribute to deriving common
endpoints like what constitutes a responder, or a Best Overall Response
of complete response (CR), … are predefined in admiralonco (see Pre-Defined Response Event
Objects). Some may need to be adjusted for study-specific needs,
e.g., minimum time between response and confirmation assessment. Here
the confirmation period and the keep_source_vars
argument
is updated.
confirmation_period <- 21
crsp_y_cr <- event_joined(
description = paste(
"Define confirmed response as CR followed by CR at least",
confirmation_period,
"days later and at most one NE in between"
),
dataset_name = "ovr",
join_vars = exprs(AVALC, ADT),
join_type = "after",
order = exprs(ADT),
first_cond_upper = AVALC.join == "CR" &
ADT.join >= ADT + days(confirmation_period),
condition = AVALC == "CR" &
all(AVALC.join %in% c("CR", "NE")) &
count_vals(var = AVALC.join, val = "NE") <= 1,
set_values_to = exprs(AVALC = "Y")
)
crsp_y_pr <- event_joined(
description = paste(
"Define confirmed response as PR followed by CR or PR at least",
confirmation_period,
"days later, at most one NE in between, and no PR after CR"
),
dataset_name = "ovr",
join_vars = exprs(AVALC, ADT),
join_type = "after",
order = exprs(ADT),
first_cond_upper = AVALC.join %in% c("CR", "PR") &
ADT.join >= ADT + days(confirmation_period),
condition = AVALC == "PR" &
all(AVALC.join %in% c("CR", "PR", "NE")) &
count_vals(var = AVALC.join, val = "NE") <= 1 &
(
min_cond(
var = ADT.join,
cond = AVALC.join == "CR"
) > max_cond(var = ADT.join, cond = AVALC.join == "PR") |
count_vals(var = AVALC.join, val = "CR") == 0 |
count_vals(var = AVALC.join, val = "PR") == 0
),
set_values_to = exprs(AVALC = "Y")
)
cbor_cr <- event_joined(
description = paste(
"Define complete response (CR) for confirmed best overall response (CBOR) as",
"CR followed by CR at least",
confirmation_period,
"days later and at most one NE in between"
),
dataset_name = "ovr",
join_vars = exprs(AVALC, ADT),
join_type = "after",
first_cond_upper = AVALC.join == "CR" &
ADT.join >= ADT + confirmation_period,
condition = AVALC == "CR" &
all(AVALC.join %in% c("CR", "NE")) &
count_vals(var = AVALC.join, val = "NE") <= 1,
set_values_to = exprs(AVALC = "CR")
)
cbor_pr <- event_joined(
description = paste(
"Define partial response (PR) for confirmed best overall response (CBOR) as",
"PR followed by CR or PR at least",
confirmation_period,
"28 days later, at most one NE in between, and no PR after CR"
),
dataset_name = "ovr",
join_vars = exprs(AVALC, ADT),
join_type = "after",
first_cond_upper = AVALC.join %in% c("CR", "PR") &
ADT.join >= ADT + confirmation_period,
condition = AVALC == "PR" &
all(AVALC.join %in% c("CR", "PR", "NE")) &
count_vals(var = AVALC.join, val = "NE") <= 1 &
(
min_cond(
var = ADT.join,
cond = AVALC.join == "CR"
) > max_cond(var = ADT.join, cond = AVALC.join == "PR") |
count_vals(var = AVALC.join, val = "CR") == 0 |
count_vals(var = AVALC.join, val = "PR") == 0
),
set_values_to = exprs(AVALC = "PR")
)
no_data_n <- event(
description = "Define no response for all patients in adsl (should be used as last event)",
dataset_name = "adsl",
condition = TRUE,
set_values_to = exprs(AVALC = "N"),
keep_source_vars = adsl_vars
)
no_data_missing <- event(
description = paste(
"Define missing response (MISSING) for all patients in adsl (should be used",
"as last event)"
),
dataset_name = "adsl",
condition = TRUE,
set_values_to = exprs(AVALC = "MISSING"),
keep_source_vars = adsl_vars
)
Now that we have the input records prepared above with any
company-specific requirements, we can start to derive new parameter
records. For the parameter derivations, all values except those
overwritten by set_values_to
argument are kept from the
earliest occurring input record fulfilling the required criteria.
The function admiral::derive_extreme_records()
can be
used to find the date of first PD
.
adrs <- adrs %>%
derive_extreme_records(
dataset_ref = adsl,
dataset_add = adrs,
by_vars = get_admiral_option("subject_keys"),
filter_add = PARAMCD == "OVR" & AVALC == "PD" & ANL01FL == "Y",
order = exprs(ADT, RSSEQ),
mode = "first",
exist_flag = AVALC,
false_value = "N",
set_values_to = exprs(
PARAMCD = "PD",
PARAM = "Disease Progression by Investigator",
PARCAT1 = "Tumor Response",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1",
AVAL = yn_to_numeric(AVALC),
ANL01FL = "Y"
)
)
USUBJID | AVISIT | PARAMCD | PARAM | AVALC | ADT | ANL01FL |
---|---|---|---|---|---|---|
01-701-1028 | WEEK 6 | PD | Disease Progression by Investigator | Y | 2013-08-30 | Y |
01-701-1130 | WEEK 9 | PD | Disease Progression by Investigator | Y | 2014-04-19 | Y |
01-701-1133 | WEEK 9 | PD | Disease Progression by Investigator | Y | 2012-12-30 | Y |
01-701-1015 | NA | PD | Disease Progression by Investigator | N | NA | Y |
01-701-1023 | NA | PD | Disease Progression by Investigator | N | NA | Y |
01-701-1034 | NA | PD | Disease Progression by Investigator | N | NA | Y |
01-701-1097 | NA | PD | Disease Progression by Investigator | N | NA | Y |
01-701-1115 | NA | PD | Disease Progression by Investigator | N | NA | Y |
01-701-1118 | NA | PD | Disease Progression by Investigator | N | NA | Y |
For progressive disease, response and death parameters shown in steps
here and below, in our examples we show these as ADRS
parameters, but they could equally be achieved via ADSL
dates or ADEVENT
parameters. If you prefer to store as an
ADSL date, then the function
admiral::derive_var_extreme_dt()
could be used to find the
date of first PD
as a variable, rather than as a new
parameter record.
The function admiral::derive_extreme_event()
can then be
used to find the date of first response. In the below example, the
response condition has been defined as CR
or
PR
via the rsp_y
1 event.
adrs <- adrs %>%
derive_extreme_event(
by_vars = get_admiral_option("subject_keys"),
order = exprs(event_nr, ADT),
tmp_event_nr_var = event_nr,
mode = "first",
events = list(rsp_y, no_data_n),
source_datasets = list(
ovr = ovr,
adsl = adsl
),
set_values_to = exprs(
PARAMCD = "RSP",
PARAM = "Response by Investigator (confirmation not required)",
PARCAT1 = "Tumor Response",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1",
AVAL = yn_to_numeric(AVALC),
ANL01FL = "Y"
)
)
USUBJID | AVISIT | PARAMCD | PARAM | AVALC | ADT | ANL01FL |
---|---|---|---|---|---|---|
01-701-1015 | WEEK 9 | RSP | Response by Investigator (confirmation not required) | Y | 2014-03-06 | Y |
01-701-1023 | NA | RSP | Response by Investigator (confirmation not required) | N | NA | Y |
01-701-1028 | NA | RSP | Response by Investigator (confirmation not required) | N | NA | Y |
01-701-1034 | NA | RSP | Response by Investigator (confirmation not required) | N | NA | Y |
01-701-1097 | NA | RSP | Response by Investigator (confirmation not required) | N | NA | Y |
01-701-1115 | WEEK 6 | RSP | Response by Investigator (confirmation not required) | Y | 2013-01-11 | Y |
01-701-1118 | WEEK 6 | RSP | Response by Investigator (confirmation not required) | Y | 2014-04-23 | Y |
01-701-1130 | NA | RSP | Response by Investigator (confirmation not required) | N | NA | Y |
01-701-1133 | WEEK 3 | RSP | Response by Investigator (confirmation not required) | Y | 2012-11-18 | Y |
The function admiral::derive_extreme_event()
can then be
used to derive the clinical benefit parameter, which we define as a
patient having had a response or a sustained period of time before first
PD
. This could also be known as disease control. In this
example the “sustained period” has been defined as 42 days after
randomization date via the cb_y
2 event.
Please note that the result AVALC = "Y"
is defined by
the first two events specified for events
. For
subjects with observations fulfilling both events the one with the
earlier date should be selected (and not the first one in the list).
Thus ignore_event_order = TRUE
is specified.
adrs <- adrs %>%
derive_extreme_event(
by_vars = get_admiral_option("subject_keys"),
order = exprs(desc(AVALC), ADT, event_nr),
tmp_event_nr_var = event_nr,
mode = "first",
events = list(rsp_y, cb_y, no_data_n),
source_datasets = list(
ovr = ovr,
adsl = adsl
),
set_values_to = exprs(
PARAMCD = "CB",
PARAM = "Clinical Benefit by Investigator (confirmation for response not required)",
PARCAT1 = "Tumor Response",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1",
AVAL = yn_to_numeric(AVALC),
ANL01FL = "Y"
)
)
USUBJID | AVISIT | PARAMCD | PARAM | AVALC | ADT | RANDDT | ANL01FL |
---|---|---|---|---|---|---|---|
01-701-1015 | WEEK 9 | CB | Clinical Benefit by Investigator (confirmation for response not required) | Y | 2014-03-06 | 2014-01-02 | Y |
01-701-1023 | NA | CB | Clinical Benefit by Investigator (confirmation for response not required) | N | NA | 2012-08-05 | Y |
01-701-1028 | NA | CB | Clinical Benefit by Investigator (confirmation for response not required) | N | NA | 2013-07-19 | Y |
01-701-1034 | WEEK 6 | CB | Clinical Benefit by Investigator (confirmation for response not required) | Y | 2014-08-12 | 2014-07-01 | Y |
01-701-1097 | NA | CB | Clinical Benefit by Investigator (confirmation for response not required) | N | NA | 2014-01-01 | Y |
01-701-1115 | WEEK 6 | CB | Clinical Benefit by Investigator (confirmation for response not required) | Y | 2013-01-11 | 2012-11-30 | Y |
01-701-1118 | WEEK 6 | CB | Clinical Benefit by Investigator (confirmation for response not required) | Y | 2014-04-23 | 2014-03-12 | Y |
01-701-1130 | WEEK 6 | CB | Clinical Benefit by Investigator (confirmation for response not required) | Y | 2014-03-29 | 2014-02-15 | Y |
01-701-1133 | WEEK 3 | CB | Clinical Benefit by Investigator (confirmation for response not required) | Y | 2012-11-18 | 2012-10-28 | Y |
The function admiral::derive_extreme_event()
can be used
to derive the best overall response (without confirmation required)
parameter. Similar to the above function you can optionally decide what
period would you consider a SD
or
NON-CR/NON-PD
as being eligible from. In this example, 42
days after randomization date has been used again.
Please note that the order of the events specified for
events
is important. For example, a subject with
PR
, PR
, CR
qualifies for both
bor_cr
and bor_pr
. As bor_cr
is
listed before bor_pr
, CR is selected as best overall
response for this subject.
adrs <- adrs %>%
derive_extreme_event(
by_vars = get_admiral_option("subject_keys"),
order = exprs(event_nr, ADT),
tmp_event_nr_var = event_nr,
mode = "first",
source_datasets = list(
ovr = ovr,
adsl = adsl
),
events = list(bor_cr, bor_pr, bor_sd, bor_non_crpd, bor_pd, bor_ne, no_data_missing),
set_values_to = exprs(
PARAMCD = "BOR",
PARAM = "Best Overall Response by Investigator (confirmation not required)",
PARCAT1 = "Tumor Response",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1",
AVAL = aval_resp(AVALC),
ANL01FL = "Y"
)
)
USUBJID | AVISIT | PARAMCD | PARAM | AVALC | ADT | RANDDT | ANL01FL |
---|---|---|---|---|---|---|---|
01-701-1015 | WEEK 9 | BOR | Best Overall Response by Investigator (confirmation not required) | CR | 2014-03-06 | 2014-01-02 | Y |
01-701-1023 | NA | BOR | Best Overall Response by Investigator (confirmation not required) | MISSING | NA | 2012-08-05 | Y |
01-701-1028 | WEEK 6 | BOR | Best Overall Response by Investigator (confirmation not required) | PD | 2013-08-30 | 2013-07-19 | Y |
01-701-1034 | WEEK 6 | BOR | Best Overall Response by Investigator (confirmation not required) | NON-CR/NON-PD | 2014-08-12 | 2014-07-01 | Y |
01-701-1097 | WEEK 3 | BOR | Best Overall Response by Investigator (confirmation not required) | NE | 2014-01-22 | 2014-01-01 | Y |
01-701-1115 | WEEK 9 | BOR | Best Overall Response by Investigator (confirmation not required) | CR | 2013-02-01 | 2012-11-30 | Y |
01-701-1118 | WEEK 6 | BOR | Best Overall Response by Investigator (confirmation not required) | PR | 2014-04-23 | 2014-03-12 | Y |
01-701-1130 | WEEK 6 | BOR | Best Overall Response by Investigator (confirmation not required) | SD | 2014-03-29 | 2014-02-15 | Y |
01-701-1133 | WEEK 6 | BOR | Best Overall Response by Investigator (confirmation not required) | CR | 2012-12-09 | 2012-10-28 | Y |
Note that the above gives pre-defined AVAL
values
(defined by aval_resp()
) of: "CR" ~ 1
,
"PR" ~ 2
, "SD" ~ 3
,
"NON-CR/NON-PD" ~ 4
, "PD" ~ 5
,
"NE" ~ 6
, "MISSING" ~ 7
.
If you’d like to provide your own company-specific ordering here you could do this as follows:
aval_resp_new <- function(arg) {
case_when(
arg == "CR" ~ 7,
arg == "PR" ~ 6,
arg == "SD" ~ 5,
arg == "NON-CR/NON-PD" ~ 4,
arg == "PD" ~ 3,
arg == "NE" ~ 2,
arg == "MISSING" ~ 1,
TRUE ~ NA_real_
)
}
Then update the definition of AVAL
in the
set_values_to
argument of the above
admiral::derive_extreme_event()
call. Be aware that this
will only impact the AVAL
mapping, not the derivation of
BOR in any way - as the function derivation relies only on the events
and their order specified for the events
argument here.
The function admiral::derive_extreme_records()
can be
used to check if a patient had a response for BOR.
adrs <- adrs %>%
derive_extreme_records(
dataset_ref = adsl,
dataset_add = adrs,
by_vars = get_admiral_option("subject_keys"),
filter_add = PARAMCD == "BOR" & AVALC %in% c("CR", "PR"),
exist_flag = AVALC,
false_value = "N",
set_values_to = exprs(
PARAMCD = "BCP",
PARAM = "Best Overall Response of CR/PR by Investigator (confirmation not required)",
PARCAT1 = "Tumor Response",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1",
AVAL = yn_to_numeric(AVALC),
ANL01FL = "Y"
)
)
USUBJID | AVISIT | PARAMCD | PARAM | AVALC | ADT | ANL01FL |
---|---|---|---|---|---|---|
01-701-1015 | WEEK 9 | BCP | Best Overall Response of CR/PR by Investigator (confirmation not required) | Y | 2014-03-06 | Y |
01-701-1115 | WEEK 9 | BCP | Best Overall Response of CR/PR by Investigator (confirmation not required) | Y | 2013-02-01 | Y |
01-701-1118 | WEEK 6 | BCP | Best Overall Response of CR/PR by Investigator (confirmation not required) | Y | 2014-04-23 | Y |
01-701-1133 | WEEK 6 | BCP | Best Overall Response of CR/PR by Investigator (confirmation not required) | Y | 2012-12-09 | Y |
01-701-1023 | NA | BCP | Best Overall Response of CR/PR by Investigator (confirmation not required) | N | NA | Y |
01-701-1028 | NA | BCP | Best Overall Response of CR/PR by Investigator (confirmation not required) | N | NA | Y |
01-701-1034 | NA | BCP | Best Overall Response of CR/PR by Investigator (confirmation not required) | N | NA | Y |
01-701-1097 | NA | BCP | Best Overall Response of CR/PR by Investigator (confirmation not required) | N | NA | Y |
01-701-1130 | NA | BCP | Best Overall Response of CR/PR by Investigator (confirmation not required) | N | NA | Y |
Any of the above response parameters can be repeated for “confirmed”
responses only. For these the function
admiral::derive_extreme_event()
can be used with different
events. Some of the other functions from above can then be re-used
passing in these confirmed response records. See the examples below of
derived parameters requiring confirmation. The assessment and the
confirmatory assessment here need to occur at least 28 days apart
(without any +1 applied to this calculation of days between
visits), using the crsp_y_cr
3,
crsp_y_pr
4, cbor_cr
5, and
cbor_pr
6 event.
Please note that the result AVALC = "Y"
for confirmed
clinical benefit is defined by the first two events specified
for events
. For subjects with observations fulfilling both
events the one with the earlier date should be selected (and not the
first one in the list). Thus ignore_event_order = TRUE
is
specified.
adrs <- adrs %>%
derive_extreme_event(
by_vars = get_admiral_option("subject_keys"),
order = exprs(desc(AVALC), ADT, event_nr),
tmp_event_nr_var = event_nr,
mode = "first",
source_datasets = list(
ovr = ovr,
adsl = adsl
),
events = list(crsp_y_cr, crsp_y_pr, no_data_n),
set_values_to = exprs(
PARAMCD = "CRSP",
PARAM = "Confirmed Response by Investigator",
PARCAT1 = "Tumor Response",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1",
AVAL = yn_to_numeric(AVALC),
ANL01FL = "Y"
)
)
adrs <- adrs %>%
derive_extreme_event(
by_vars = get_admiral_option("subject_keys"),
order = exprs(desc(AVALC), ADT, event_nr),
tmp_event_nr_var = event_nr,
mode = "first",
events = list(crsp_y_cr, crsp_y_pr, cb_y, no_data_n),
source_datasets = list(
ovr = ovr,
adsl = adsl
),
set_values_to = exprs(
PARAMCD = "CCB",
PARAM = "Confirmed Clinical Benefit by Investigator",
PARCAT1 = "Tumor Response",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1",
AVAL = yn_to_numeric(AVALC),
ANL01FL = "Y"
)
)
adrs <- adrs %>%
derive_extreme_event(
by_vars = get_admiral_option("subject_keys"),
order = exprs(event_nr, ADT),
tmp_event_nr_var = event_nr,
mode = "first",
events = list(cbor_cr, cbor_pr, bor_sd, bor_non_crpd, bor_pd, bor_ne, no_data_missing),
source_datasets = list(
ovr = ovr,
adsl = adsl
),
set_values_to = exprs(
PARAMCD = "CBOR",
PARAM = "Best Confirmed Overall Response by Investigator",
PARCAT1 = "Tumor Response",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1",
AVAL = aval_resp(AVALC),
ANL01FL = "Y"
)
) %>%
derive_extreme_records(
dataset_ref = adsl,
dataset_add = adrs,
by_vars = get_admiral_option("subject_keys"),
filter_add = PARAMCD == "CBOR" & AVALC %in% c("CR", "PR"),
exist_flag = AVALC,
false_value = "N",
set_values_to = exprs(
PARAMCD = "CBCP",
PARAM = "Best Confirmed Overall Response of CR/PR by Investigator",
PARCAT1 = "Tumor Response",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1",
AVAL = yn_to_numeric(AVALC),
ANL01FL = "Y"
)
)
USUBJID | AVISIT | PARAMCD | PARAM | AVALC | ADT | RANDDT | ANL01FL |
---|---|---|---|---|---|---|---|
01-701-1015 | NA | CRSP | Confirmed Response by Investigator | N | NA | 2014-01-02 | Y |
01-701-1023 | NA | CRSP | Confirmed Response by Investigator | N | NA | 2012-08-05 | Y |
01-701-1028 | NA | CRSP | Confirmed Response by Investigator | N | NA | 2013-07-19 | Y |
01-701-1034 | NA | CRSP | Confirmed Response by Investigator | N | NA | 2014-07-01 | Y |
01-701-1097 | NA | CRSP | Confirmed Response by Investigator | N | NA | 2014-01-01 | Y |
01-701-1115 | WEEK 6 | CRSP | Confirmed Response by Investigator | Y | 2013-01-11 | 2012-11-30 | Y |
01-701-1118 | WEEK 6 | CRSP | Confirmed Response by Investigator | Y | 2014-04-23 | 2014-03-12 | Y |
01-701-1130 | NA | CRSP | Confirmed Response by Investigator | N | NA | 2014-02-15 | Y |
01-701-1133 | WEEK 3 | CRSP | Confirmed Response by Investigator | Y | 2012-11-18 | 2012-10-28 | Y |
01-701-1015 | WEEK 9 | CCB | Confirmed Clinical Benefit by Investigator | Y | 2014-03-06 | 2014-01-02 | Y |
As admiral::derive_extreme_event()
is very flexible, it
is easy to implement non-standard parameters. Below two examples for
modified RECIST 1.1 parameters are shown.
Confirmed clinical benefit was defined before as confirmed response or CR, PR, SD, or NON-CR/NON-PD at least 42 days after randomization. Here an alternative definition is implemented which considers PD more than 42 days after randomization as an additional criterion for clinical benefit.
cb_y_pd <- event(
description = paste(
"Define PD occuring more than 42 days after",
"randomization as clinical benefit"
),
dataset_name = "ovr",
condition = AVALC == "PD" & ADT > RANDDT + 42,
set_values_to = exprs(AVALC = "Y")
)
adrs <- adrs %>%
derive_extreme_event(
by_vars = get_admiral_option("subject_keys"),
order = exprs(desc(AVALC), ADT, event_nr),
tmp_event_nr_var = event_nr,
mode = "first",
events = list(crsp_y_cr, crsp_y_pr, cb_y, cb_y_pd, no_data_n),
source_datasets = list(
ovr = ovr,
adsl = adsl
),
set_values_to = exprs(
PARAMCD = "ACCB",
PARAM = "Alternative Confirmed Clinical Benefit by Investigator",
PARCAT1 = "Tumor Response",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1",
AVAL = yn_to_numeric(AVALC),
ANL01FL = "Y"
)
)
Assume no evidence of disease (NED) is a valid value collected for
overall response. A new event (bor_ned
) can be defined for
this response value and be added to the list of events
(events
) in the
admiral::derive_extreme_event()
call.
bor_ned <- event(
description = paste(
"Define no evidence of disease (NED) for best overall response (BOR) as NED",
"occuring at least 42 days after randomization"
),
dataset_name = "ovr",
condition = AVALC == "NED" & ADT >= RANDDT + 42,
set_values_to = exprs(AVALC = "NED")
)
adrs <- adrs %>%
derive_extreme_event(
by_vars = get_admiral_option("subject_keys"),
order = exprs(event_nr, ADT),
tmp_event_nr_var = event_nr,
mode = "first",
source_datasets = list(
ovr = ovr,
adsl = adsl
),
events = list(bor_cr, bor_pr, bor_sd, bor_non_crpd, bor_ned, bor_pd, bor_ne, no_data_missing),
set_values_to = exprs(
PARAMCD = "A1BOR",
PARAM = paste(
"Best Overall Response by Investigator (confirmation not required)",
"- RECIST 1.1 adjusted for NED at Baseline"
),
PARCAT1 = "Tumor Response",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1 adjusted for NED at Baseline",
AVAL = aval_resp(AVALC),
ANL01FL = "Y"
)
)
All of the above steps can be repeated for different sets of records,
such as now using assessments from the IRF/BICR instead of investigator.
For this you would just need to replace the first steps with selecting
the required records, create the variables AVALC
,
AVAL
, ADT
, AVISIT
,
ANL01FL
, ANL02FL
and the dataset
ovrb
(see Pre-processing of Input
Records) and then feed these as input to the downstream parameter
functions.
adrs_bicr <- rs %>%
filter(
RSEVAL == "INDEPENDENT ASSESSOR" & RSACPTFL == "Y" & RSTESTCD == "OVRLRESP"
) %>%
mutate(
PARAMCD = "OVRB",
PARAM = "Overall Response by BICR",
PARCAT1 = "Tumor Response",
PARCAT2 = "Blinded Independent Central Review",
PARCAT3 = "RECIST 1.1"
)
USUBJID | VISIT | RSTESTCD | RSEVAL | PARAMCD | PARAM | PARCAT1 | PARCAT2 | PARCAT3 |
---|---|---|---|---|---|---|---|---|
01-701-1015 | WEEK 3 | OVRLRESP | INDEPENDENT ASSESSOR | OVRB | Overall Response by BICR | Tumor Response | Blinded Independent Central Review | RECIST 1.1 |
01-701-1015 | WEEK 6 | OVRLRESP | INDEPENDENT ASSESSOR | OVRB | Overall Response by BICR | Tumor Response | Blinded Independent Central Review | RECIST 1.1 |
01-701-1015 | WEEK 9 | OVRLRESP | INDEPENDENT ASSESSOR | OVRB | Overall Response by BICR | Tumor Response | Blinded Independent Central Review | RECIST 1.1 |
01-701-1028 | WEEK 3 | OVRLRESP | INDEPENDENT ASSESSOR | OVRB | Overall Response by BICR | Tumor Response | Blinded Independent Central Review | RECIST 1.1 |
01-701-1028 | WEEK 6 | OVRLRESP | INDEPENDENT ASSESSOR | OVRB | Overall Response by BICR | Tumor Response | Blinded Independent Central Review | RECIST 1.1 |
01-701-1028 | WEEK 9 | OVRLRESP | INDEPENDENT ASSESSOR | OVRB | Overall Response by BICR | Tumor Response | Blinded Independent Central Review | RECIST 1.1 |
01-701-1034 | WEEK 3 | OVRLRESP | INDEPENDENT ASSESSOR | OVRB | Overall Response by BICR | Tumor Response | Blinded Independent Central Review | RECIST 1.1 |
01-701-1034 | WEEK 6 | OVRLRESP | INDEPENDENT ASSESSOR | OVRB | Overall Response by BICR | Tumor Response | Blinded Independent Central Review | RECIST 1.1 |
01-701-1097 | WEEK 3 | OVRLRESP | INDEPENDENT ASSESSOR | OVRB | Overall Response by BICR | Tumor Response | Blinded Independent Central Review | RECIST 1.1 |
01-701-1115 | WEEK 3 | OVRLRESP | INDEPENDENT ASSESSOR | OVRB | Overall Response by BICR | Tumor Response | Blinded Independent Central Review | RECIST 1.1 |
Then in all the calls to the parameter derivation functions you would
replace ovr = ovr
with ovr == ovrb
in the
value of the source_datasets
argument.
The function admiral::derive_extreme_records()
can be
used to create a new death parameter using death date from
ADSL
. We need to restrict the columns from
ADSL
as we’ll merge all required variables later across all
our ADRS
records.
adsldth <- adsl %>%
select(!!!get_admiral_option("subject_keys"), DTHDT, !!!adsl_vars)
adrs <- adrs %>%
derive_extreme_records(
dataset_ref = adsldth,
dataset_add = adsldth,
by_vars = get_admiral_option("subject_keys"),
filter_add = !is.na(DTHDT),
exist_flag = AVALC,
false_value = "N",
set_values_to = exprs(
PARAMCD = "DEATH",
PARAM = "Death",
PARCAT1 = "Reference Event",
AVAL = yn_to_numeric(AVALC),
ANL01FL = "Y",
ADT = DTHDT
)
) %>%
select(-DTHDT)
USUBJID | AVISIT | PARAMCD | PARAM | AVALC | ADT | ANL01FL |
---|---|---|---|---|---|---|
01-701-1015 | NA | DEATH | Death | N | NA | Y |
01-701-1023 | NA | DEATH | Death | N | NA | Y |
01-701-1028 | NA | DEATH | Death | N | NA | Y |
01-701-1034 | NA | DEATH | Death | N | NA | Y |
01-701-1097 | NA | DEATH | Death | N | NA | Y |
01-701-1115 | NA | DEATH | Death | N | NA | Y |
01-701-1118 | NA | DEATH | Death | N | NA | Y |
01-701-1130 | NA | DEATH | Death | N | NA | Y |
01-701-1133 | NA | DEATH | Death | N | NA | Y |
The function admiral::derive_extreme_records()
can be
used to create a parameter for last disease assessment.
adrs <- adrs %>%
derive_extreme_records(
dataset_ref = adsl,
dataset_add = adrs,
by_vars = get_admiral_option("subject_keys"),
filter_add = PARAMCD == "OVR" & ANL01FL == "Y",
order = exprs(ADT, RSSEQ),
mode = "last",
set_values_to = exprs(
PARAMCD = "LSTA",
PARAM = "Last Disease Assessment by Investigator",
PARCAT1 = "Tumor Response",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1",
ANL01FL = "Y"
)
)
USUBJID | AVISIT | PARAMCD | PARAM | AVALC | ADT | ANL01FL |
---|---|---|---|---|---|---|
01-701-1015 | WEEK 9 | LSTA | Last Disease Assessment by Investigator | CR | 2014-03-06 | Y |
01-701-1028 | WEEK 9 | LSTA | Last Disease Assessment by Investigator | SD | 2013-09-20 | Y |
01-701-1034 | WEEK 6 | LSTA | Last Disease Assessment by Investigator | NON-CR/NON-PD | 2014-08-12 | Y |
01-701-1097 | WEEK 3 | LSTA | Last Disease Assessment by Investigator | NON-CR/NON-PD | 2014-01-22 | Y |
01-701-1115 | WEEK 9 | LSTA | Last Disease Assessment by Investigator | CR | 2013-02-01 | Y |
01-701-1118 | WEEK 12 | LSTA | Last Disease Assessment by Investigator | PR | 2014-06-04 | Y |
01-701-1130 | WEEK 9 | LSTA | Last Disease Assessment by Investigator | PD | 2014-04-19 | Y |
01-701-1133 | WEEK 9 | LSTA | Last Disease Assessment by Investigator | PD | 2012-12-30 | Y |
01-701-1023 | NA | LSTA | Last Disease Assessment by Investigator | NA | NA | Y |
The function admiral::derive_param_exist_flag()
can be
used to check whether a patient has measurable disease at baseline,
according to a company-specific condition. In this example we check
TU
for target lesions during the baseline visit. We need to
restrict the columns from ADSL
as we’ll merge all required
variables later across all our ADRS
records.
adslmdis <- adsl %>%
select(!!!get_admiral_option("subject_keys"), !!!adsl_vars)
adrs <- adrs %>%
derive_param_exist_flag(
dataset_ref = adslmdis,
dataset_add = tu,
condition = TUEVAL == "INVESTIGATOR" & TUSTRESC == "TARGET" & VISIT == "SCREENING",
false_value = "N",
missing_value = "N",
set_values_to = exprs(
PARAMCD = "MDIS",
PARAM = "Measurable Disease at Baseline by Investigator",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1",
AVAL = yn_to_numeric(AVALC),
ANL01FL = "Y"
)
)
USUBJID | AVISIT | PARAMCD | PARAM | AVALC | ADT | ANL01FL |
---|---|---|---|---|---|---|
01-701-1015 | NA | MDIS | Measurable Disease at Baseline by Investigator | Y | NA | Y |
01-701-1023 | NA | MDIS | Measurable Disease at Baseline by Investigator | N | NA | Y |
01-701-1028 | NA | MDIS | Measurable Disease at Baseline by Investigator | Y | NA | Y |
01-701-1034 | NA | MDIS | Measurable Disease at Baseline by Investigator | N | NA | Y |
01-701-1097 | NA | MDIS | Measurable Disease at Baseline by Investigator | N | NA | Y |
01-701-1115 | NA | MDIS | Measurable Disease at Baseline by Investigator | Y | NA | Y |
01-701-1118 | NA | MDIS | Measurable Disease at Baseline by Investigator | Y | NA | Y |
01-701-1130 | NA | MDIS | Measurable Disease at Baseline by Investigator | Y | NA | Y |
01-701-1133 | NA | MDIS | Measurable Disease at Baseline by Investigator | Y | NA | Y |
ASEQ
The function admiral::derive_var_obs_number()
can be
used to derive ASEQ
. An example call is:
adrs <- adrs %>%
derive_var_obs_number(
by_vars = get_admiral_option("subject_keys"),
order = exprs(PARAMCD, ADT, VISITNUM, RSSEQ),
check_type = "error"
)
USUBJID | PARAMCD | ADT | VISITNUM | AVISIT | ASEQ |
---|---|---|---|---|---|
01-701-1015 | A1BOR | 2014-03-06 | 4 | WEEK 9 | 1 |
01-701-1015 | ACCB | 2014-03-06 | 4 | WEEK 9 | 2 |
01-701-1015 | BCP | 2014-03-06 | 4 | WEEK 9 | 3 |
01-701-1015 | BOR | 2014-03-06 | 4 | WEEK 9 | 4 |
01-701-1015 | CB | 2014-03-06 | 4 | WEEK 9 | 5 |
01-701-1015 | CBCP | NA | NA | NA | 6 |
01-701-1015 | CBOR | 2014-03-06 | 4 | WEEK 9 | 7 |
01-701-1015 | CCB | 2014-03-06 | 4 | WEEK 9 | 8 |
01-701-1015 | CRSP | NA | NA | NA | 9 |
01-701-1015 | DEATH | NA | NA | NA | 10 |
If needed, the other ADSL
variables can now be added.
List of ADSL variables already merged held in vector
adsl_vars
.
adrs <- adrs %>%
derive_vars_merged(
dataset_add = select(adsl, !!!negate_vars(adsl_vars)),
by_vars = get_admiral_option("subject_keys")
)
USUBJID | RFSTDTC | RFENDTC | DTHDTC | DTHFL | AGE | AGEU |
---|---|---|---|---|---|---|
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
01-701-1015 | 2014-01-02 | 2014-07-02 | NA | NA | 63 | YEARS |
ADaM | Sample Code |
---|---|
ADRS | admiral::use_ad_template("ADRS", package = "admiralonco") |
rsp_y
#> <event> object
#> description: "Define CR or PR as (unconfirmed) response"
#> dataset_name: "ovr"
#> condition: AVALC %in% c("CR", "PR")
#> mode: NULL
#> order: NULL
#> set_values_to:
#> AVALC: "Y"
#> keep_source_vars: NULL
cb_y
#> <event> object
#> description: "Define CR, PR, SD, or NON-CR/NON-PD occuring at least 42 days after randomization as clinical benefit"
#> dataset_name: "ovr"
#> condition: AVALC %in% c("CR", "PR", "SD", "NON-CR/NON-PD") & ADT >= RANDDT +
#> 42
#> mode: NULL
#> order: NULL
#> set_values_to:
#> AVALC: "Y"
#> keep_source_vars: NULL
crsp_y_cr
#> <event_joined> object
#> description: "Define confirmed response as CR followed by CR at least 21 days later and at most one NE in between"
#> dataset_name: "ovr"
#> condition: AVALC == "CR" & all(AVALC.join %in% c("CR", "NE")) & count_vals(var = AVALC.join,
#> val = "NE") <= 1
#> order:
#> ADT
#> join_vars:
#> AVALC
#> ADT
#> join_type: "after"
#> first_cond_lower: NULL
#> first_cond_upper: AVALC.join == "CR" & ADT.join >= ADT + days(confirmation_period)
#> set_values_to:
#> AVALC: "Y"
#> keep_source_vars: NULL
crsp_y_pr
#> <event_joined> object
#> description: "Define confirmed response as PR followed by CR or PR at least 21 days later, at most one NE in between, and no PR after CR"
#> dataset_name: "ovr"
#> condition: AVALC == "PR" & all(AVALC.join %in% c("CR", "PR", "NE")) & count_vals(var = AVALC.join,
#> val = "NE") <= 1 & (min_cond(var = ADT.join, cond = AVALC.join ==
#> "CR") > max_cond(var = ADT.join, cond = AVALC.join == "PR") |
#> count_vals(var = AVALC.join, val = "CR") == 0 | count_vals(var = AVALC.join,
#> val = "PR") == 0)
#> order:
#> ADT
#> join_vars:
#> AVALC
#> ADT
#> join_type: "after"
#> first_cond_lower: NULL
#> first_cond_upper: AVALC.join %in% c("CR", "PR") & ADT.join >= ADT + days(confirmation_period)
#> set_values_to:
#> AVALC: "Y"
#> keep_source_vars: NULL
cbor_cr
#> <event_joined> object
#> description: "Define complete response (CR) for confirmed best overall response (CBOR) as CR followed by CR at least 21 days later and at most one NE in between"
#> dataset_name: "ovr"
#> condition: AVALC == "CR" & all(AVALC.join %in% c("CR", "NE")) & count_vals(var = AVALC.join,
#> val = "NE") <= 1
#> order: NULL
#> join_vars:
#> AVALC
#> ADT
#> join_type: "after"
#> first_cond_lower: NULL
#> first_cond_upper: AVALC.join == "CR" & ADT.join >= ADT + confirmation_period
#> set_values_to:
#> AVALC: "CR"
#> keep_source_vars: NULL
cbor_pr
#> <event_joined> object
#> description: "Define partial response (PR) for confirmed best overall response (CBOR) as PR followed by CR or PR at least 21 28 days later, at most one NE in between, and no PR after CR"
#> dataset_name: "ovr"
#> condition: AVALC == "PR" & all(AVALC.join %in% c("CR", "PR", "NE")) & count_vals(var = AVALC.join,
#> val = "NE") <= 1 & (min_cond(var = ADT.join, cond = AVALC.join ==
#> "CR") > max_cond(var = ADT.join, cond = AVALC.join == "PR") |
#> count_vals(var = AVALC.join, val = "CR") == 0 | count_vals(var = AVALC.join,
#> val = "PR") == 0)
#> order: NULL
#> join_vars:
#> AVALC
#> ADT
#> join_type: "after"
#> first_cond_lower: NULL
#> first_cond_upper: AVALC.join %in% c("CR", "PR") & ADT.join >= ADT + confirmation_period
#> set_values_to:
#> AVALC: "PR"
#> keep_source_vars: NULL
ASEQ