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}
—are used.
library(admiral)
library(admiralonco)
library(dplyr)
library(pharmaversesdtm)
library(lubridate)
library(stringr)
data("admiral_adsl")
data("rs_onco_recist")
data("tu_onco_recist")
<- admiral_adsl
adsl <- rs_onco_recist
rs <- tu_onco_recist
tu
<- convert_blanks_to_na(rs)
rs <- convert_blanks_to_na(tu) 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.
<- exprs(RANDDT)
adsl_vars <- derive_vars_merged(
adrs
rs,dataset_add = adsl,
new_vars = adsl_vars,
by_vars = exprs(STUDYID, USUBJID)
)
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.
<- adrs %>%
adrs mutate(
AVALC = RSSTRESC,
AVAL = aval_resp(AVALC)
)
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.
<- adrs %>%
adrs restrict_derivation(
derivation = derive_var_extreme_flag,
args = params(
by_vars = exprs(STUDYID, USUBJID, ADT),
order = exprs(AVAL, 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).
<- adrs %>%
adrs mutate(
ANL01FL = case_when(
!is.na(AVAL) & ADT >= RANDDT & ADT < NACTDT ~ "Y",
TRUE ~ NA_character_
) )
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 = exprs(STUDYID, USUBJID),
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.
<- filter(adrs, PARAMCD == "OVR" & ANL01FL == "Y" & ANL02FL == "Y") ovr
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.
<- 21
confirmation_period
<- event_joined(
crsp_y_cr 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 = AVALC.join == "CR" &
>= ADT + days(confirmation_period),
ADT.join condition = AVALC == "CR" &
all(AVALC.join %in% c("CR", "NE")) &
count_vals(var = AVALC.join, val = "NE") <= 1,
set_values_to = exprs(AVALC = "Y")
)
<- event_joined(
crsp_y_pr 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 = AVALC.join %in% c("CR", "PR") &
>= ADT + days(confirmation_period),
ADT.join 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")
)
<- event_joined(
cbor_cr 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 = AVALC.join == "CR" &
>= ADT + confirmation_period,
ADT.join condition = AVALC == "CR" &
all(AVALC.join %in% c("CR", "NE")) &
count_vals(var = AVALC.join, val = "NE") <= 1,
set_values_to = exprs(AVALC = "CR")
)
<- event_joined(
cbor_pr 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 = AVALC.join %in% c("CR", "PR") &
>= ADT + confirmation_period,
ADT.join 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")
)
<- event(
no_data_n 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
)
<- event(
no_data_missing 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 = exprs(STUDYID, USUBJID),
filter_add = PARAMCD == "OVR" & AVALC == "PD" & ANL01FL == "Y",
order = exprs(ADT, RSSEQ),
mode = "first",
exist_flag = AVALC,
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 = exprs(STUDYID, USUBJID),
order = exprs(ADT),
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 = exprs(STUDYID, USUBJID),
order = exprs(desc(AVALC), ADT),
mode = "first",
events = list(rsp_y, cb_y, no_data_n),
source_datasets = list(
ovr = ovr,
adsl = adsl
),ignore_event_order = TRUE,
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"
),check_type = "none"
)
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 = exprs(STUDYID, USUBJID),
order = exprs(ADT),
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:
<- function(arg) {
aval_resp_new case_when(
== "CR" ~ 7,
arg == "PR" ~ 6,
arg == "SD" ~ 5,
arg == "NON-CR/NON-PD" ~ 4,
arg == "PD" ~ 3,
arg == "NE" ~ 2,
arg == "MISSING" ~ 1,
arg 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 = exprs(STUDYID, USUBJID),
filter_add = PARAMCD == "BOR" & AVALC %in% c("CR", "PR"),
exist_flag = AVALC,
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 = exprs(STUDYID, USUBJID),
order = exprs(desc(AVALC), ADT),
mode = "first",
source_datasets = list(
ovr = ovr,
adsl = adsl
),events = list(crsp_y_cr, crsp_y_pr, no_data_n),
ignore_event_order = TRUE,
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 = exprs(STUDYID, USUBJID),
order = exprs(desc(AVALC), ADT),
mode = "first",
events = list(crsp_y_cr, crsp_y_pr, cb_y, no_data_n),
source_datasets = list(
ovr = ovr,
adsl = adsl
),ignore_event_order = TRUE,
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"
),check_type = "none"
)
<- adrs %>%
adrs derive_extreme_event(
by_vars = exprs(STUDYID, USUBJID),
order = exprs(ADT),
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 = exprs(STUDYID, USUBJID),
filter_add = PARAMCD == "CBOR" & AVALC %in% c("CR", "PR"),
exist_flag = AVALC,
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.
<- event(
cb_y_pd 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 = exprs(STUDYID, USUBJID),
order = exprs(desc(AVALC), ADT),
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
),ignore_event_order = TRUE,
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"
),check_type = "none"
)
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.
<- event(
bor_ned 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 = exprs(STUDYID, USUBJID),
order = exprs(ADT),
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.
<- rs %>%
adrs_bicr filter(
== "INDEPENDENT ASSESSOR" & RSACPTFL == "Y" & RSTESTCD == "OVRLRESP"
RSEVAL %>%
) 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.
<- adsl %>%
adsldth select(STUDYID, USUBJID, DTHDT, !!!adsl_vars)
<- adrs %>%
adrs derive_extreme_records(
dataset_ref = adsldth,
dataset_add = adsldth,
by_vars = exprs(STUDYID, USUBJID),
filter_add = !is.na(DTHDT),
exist_flag = AVALC,
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 = exprs(STUDYID, USUBJID),
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.
<- adsl %>%
adslmdis select(STUDYID, USUBJID, !!!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 = exprs(STUDYID, USUBJID),
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 = exprs(STUDYID, USUBJID)
)
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 | ad_adrs.R |
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: 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: 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: 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: AVALC.join %in% c("CR", "PR") & ADT.join >= ADT + confirmation_period
#> set_values_to:
#> AVALC: "PR"
#> keep_source_vars: NULL