This article describes creating an ADTR
(Tumor Results)
ADaM with common oncology parameters based on RECIST v1.1.
The main part in programming a tumor results dataset is the
calculation of the sum of diameters of all target lesions (lymph nodes
& non-lymph nodes), the calculation of nadir, change &
percentage change from baseline, and the analysis flags that could be
required for reporting. The tumor results data could be set up for
investigator and/or Independent review facility (IRF)/Blinded
Independent Central Review (BICR) data. The below sample code would need
to be updated (for example, update the Evaluator TR.TREVAL
,
TU.TUEVAL
, and the applicable parameter details
PARAM
, PARAMCD
, PARCATy
) in order
to create the acquired data for Independent review facility
(IRF)/Blinded Independent Central Review (BICR).
The source dataset used will depend on each company, this could be
solely the TR
domain or you may merge TU
with
TR
(SDTM inputs) to get additional data variables or to
ensure that you’re processing the same lesions as collected.
Individual lesion diameters for each target lesion is required to
calculate the sum of diameters for all target lesions, this data could
be taken directly from TR
or additional parameters could be
created in ADTR
(or similar) depending on the additional
processing required (e.g. imputation of dates, re-labeling of visits)
and your company specifications.
The majority of the functions used here exist from
{admiral}
.
Note: All examples assume CDISC SDTM and/or ADaM format as input unless otherwise specified.
TR
with TU
and
Derive New VariablesLDIAMn
, NLDIAMn
)SDIAM
) and ANL01FL
ABLFL
,
BASE
)CHG
,
PCHG
)CRFL
, CRNFL
, PDFL
,
PDFL
ANLzzFL
ASEQ
)ADSL
VariablesTo start, all data frames needed for the creation of
ADTR
should be read into the environment. This will be a
company specific process. Some of the data frames needed may be
ADSL
, ADRS
,RS
, TU
,
TR
, SUPPTU
SUPPTR
.
For example purposes, the SDTM and ADaM datasets (based on CDISC
Pilot test data)—which are included in {pharmaversesdtm}
and {pharmaverseadam}
—are used. Also, see Handling
of Missing Values explains why we need to use the
convert_blanks_to_na()
function.
On the TR
domain we filter on where tumor assessment
short name TRTESTCD
is "LDIAM"
or
"LPERP"
. Depending on your data collection standards you
may need to update the code to filter on
TRTESTCD == "DIAMETER"
. If this is the case then the
template code where we derive parameters for lesions diameters should be
updated accordingly.
data("adsl")
data("adrs_onco")
data("rs_onco_recist")
data("tu_onco_recist")
data("tr_onco_recist")
adrs <- adrs_onco
tu <- tu_onco_recist
tr <- tr_onco_recist
rs <- rs_onco_recist
tu <- convert_blanks_to_na(tu) %>%
filter(TUEVAL == "INVESTIGATOR")
tr <- convert_blanks_to_na(tr) %>%
filter(
TREVAL == "INVESTIGATOR" & TRGRPID == "TARGET" & TRTESTCD %in% c("LDIAM", "LPERP")
)
rs <- convert_blanks_to_na(rs)
At this step, it may be useful to join ADSL
to your
TR
domain. For efficiency, only the ADSL
variables (in this example, Randomization date (RANDDT
)
used for derivations are selected at this step. The rest of the relevant
ADSL
variables would be added later.
TR
with TU
and Derive New
VariablesDepending on your company specifications you may want to merge
TU
with TR
. At this point if you require any
new variables these could be added in this step. As an example, here we
are keeping the tumor location (TULOC
), and deriving a new
variable for the tumor group site (TULOCGR1
).
tr <- derive_vars_merged(
tr,
dataset_add = tu,
new_vars = exprs(TULOC),
by_vars = c(get_admiral_option("subject_keys"), exprs(TRLNKID = TULNKID))
) %>% mutate(
TULOCGR1 = if_else(
TULOC == "LYMPH NODE",
"NODAL",
"NON-NODAL"
)
)
USUBJID | VISIT | TRLNKID | TULOC | TULOCGR1 |
---|---|---|---|---|
01-701-1015 | SCREENING | T01 | ADRENAL GLAND | NON-NODAL |
01-701-1015 | SCREENING | T02 | LYMPH NODE | NODAL |
01-701-1015 | SCREENING | T03 | BODY | NON-NODAL |
01-701-1015 | SCREENING | T04 | BONE | NON-NODAL |
01-701-1015 | SCREENING | T01 | ADRENAL GLAND | NON-NODAL |
01-701-1015 | SCREENING | T02 | LYMPH NODE | NODAL |
01-701-1015 | SCREENING | T03 | BODY | NON-NODAL |
01-701-1015 | SCREENING | T04 | BONE | NON-NODAL |
01-701-1015 | WEEK 3 | T01 | ADRENAL GLAND | NON-NODAL |
01-701-1015 | WEEK 3 | T02 | LYMPH NODE | NODAL |
Furthermore, you could create additional new variables that are
required downstream when deriving the required parameters. For example,
here we include lesion ID expected (LSEXP
) and lesion ID
assessed (LSASS
).
USUBJID | TRLNKID | VISIT | LSEXP | LSASS |
---|---|---|---|---|
01-701-1015 | T01 | SCREENING | T01 | T01 |
01-701-1015 | T02 | SCREENING | T02 | T02 |
01-701-1015 | T03 | SCREENING | T03 | T03 |
01-701-1015 | T04 | SCREENING | T04 | T04 |
01-701-1015 | T01 | SCREENING | T01 | T01 |
01-701-1015 | T02 | SCREENING | T02 | T02 |
01-701-1015 | T03 | SCREENING | T03 | T03 |
01-701-1015 | T04 | SCREENING | T04 | T04 |
01-701-1015 | T01 | WEEK 3 | T01 | T01 |
01-701-1015 | T02 | WEEK 3 | T02 | T02 |
The next step involves company-specific pre-processing of records required downstream prior to the parameter derivations, to ensure that all the associated variable processing is done once before any new parameters are derived.
In order to calculate the sum of diameter of lesions, we need to
ensure that lesions are grouped correctly, how this is done will be
based on your company specifications but individual lesions sizes could
be grouped either by using the analysis date ADT
or visit
AVISIT
. This is to ensure that each lesion size is only
counted once.
Below are some considerations depending on your analysis.
ADT
,
ADTF
, AVISIT
etcIf your data collection allows for partial dates, you could apply a
company-specific imputation rule when deriving the analysis date
ADT
(you could also add further code to handle missing
ADT
, if this is a consideration).
In the example below, we impute the missing day to the earliest
possible date to derive the analysis date ADT
, and then
re-derive the analysis day ADY
.
tr <- derive_vars_dt(
tr,
dtc = TRDTC,
new_vars_prefix = "A",
highest_imputation = "D",
date_imputation = "first"
) %>%
derive_vars_dy(
reference_date = RANDDT,
source_vars = exprs(ADT)
)
USUBJID | RANDDT | TRLNKID | TRDTC | ADT | ADTF | ADY |
---|---|---|---|---|---|---|
01-701-1015 | 2014-01-02 | T01 | 2014-01-02 | 2014-01-02 | NA | 1 |
01-701-1015 | 2014-01-02 | T02 | 2014-01-02 | 2014-01-02 | NA | 1 |
01-701-1015 | 2014-01-02 | T03 | 2014-01-02 | 2014-01-02 | NA | 1 |
01-701-1015 | 2014-01-02 | T04 | 2014-01-02 | 2014-01-02 | NA | 1 |
01-701-1015 | 2014-01-02 | T01 | 2014-01-02 | 2014-01-02 | NA | 1 |
01-701-1015 | 2014-01-02 | T02 | 2014-01-02 | 2014-01-02 | NA | 1 |
01-701-1015 | 2014-01-02 | T03 | 2014-01-02 | 2014-01-02 | NA | 1 |
01-701-1015 | 2014-01-02 | T04 | 2014-01-02 | 2014-01-02 | NA | 1 |
01-701-1015 | 2014-01-02 | T01 | 2014-01-23 | 2014-01-23 | NA | 22 |
01-701-1015 | 2014-01-02 | T02 | 2014-01-23 | 2014-01-23 | NA | 22 |
If you have data collected at unscheduled visits, these could be identified as uniquely in your SDTM. However, if this is not the case and your grouping lesions by visit then you will need to amend the below code to apply company specific algorithm to re-label unscheduled visits as per your specifications.
LDIAMn
&
NLDIAMn
)Depending on your company specifications you may either create
parameters for Target Lesion diameter for lymph nodes
(LDIAMn
) and non-lymph nodes (NLDIAMn
) in
ADTR
or similar dataset.
The below examples show the set up of the parameter details and the
analysis value & flag (AVAL
/ANL01FL
)
variables.
tr <- mutate(tr, tmp_lesion_nr = str_sub(TRLNKID, 3))
adtr <- bind_rows(
tr %>%
filter(TRTESTCD == "LDIAM") %>%
mutate(
PARAMCD = paste0("LDIAM", tmp_lesion_nr),
PARAM = paste("Target Lesion", tmp_lesion_nr, "Analysis Diameter")
),
tr %>%
filter(TRTESTCD == "LPERP") %>%
mutate(
PARAMCD = paste0("NLDIAM", tmp_lesion_nr),
PARAM = paste("Target Lesion", tmp_lesion_nr, "Analysis Perpendicular")
)
) %>%
mutate(
PARCAT1 = "Target Lesion(s)",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1",
AVAL = TRSTRESN,
ANL01FL = if_else(!is.na(AVAL), "Y", NA_character_)
) %>%
select(-tmp_lesion_nr)
USUBJID | TRLNKID | AVISIT | PARAMCD | PARAM | AVAL | ANL01FL |
---|---|---|---|---|---|---|
01-701-1015 | T01 | BASELINE | LDIAM1 | Target Lesion 1 Analysis Diameter | 21.00 | Y |
01-701-1015 | T01 | BASELINE | NLDIAM1 | Target Lesion 1 Analysis Perpendicular | 20.37 | Y |
01-701-1015 | T01 | WEEK 3 | LDIAM1 | Target Lesion 1 Analysis Diameter | 20.00 | Y |
01-701-1015 | T01 | WEEK 3 | NLDIAM1 | Target Lesion 1 Analysis Perpendicular | 19.20 | Y |
01-701-1015 | T01 | WEEK 6 | LDIAM1 | Target Lesion 1 Analysis Diameter | 20.00 | Y |
01-701-1015 | T01 | WEEK 6 | NLDIAM1 | Target Lesion 1 Analysis Perpendicular | 19.00 | Y |
01-701-1015 | T01 | WEEK 9 | LDIAM1 | Target Lesion 1 Analysis Diameter | 0.00 | Y |
01-701-1015 | T01 | WEEK 9 | NLDIAM1 | Target Lesion 1 Analysis Perpendicular | 0.00 | Y |
01-701-1015 | T02 | BASELINE | LDIAM2 | Target Lesion 2 Analysis Diameter | 33.28 | Y |
01-701-1015 | T02 | BASELINE | NLDIAM2 | Target Lesion 2 Analysis Perpendicular | 32.00 | Y |
ANL01FL
Sum of Target Lesions Diameters is the sum of the diameters of both
lymph and non-lymph nodes (sum of analysis values AVAL
from
source observations LDIAMn
& LNDIAMn
).
In this vignette, the sum of the diameters is calculated across all
the available lesion measurements, and we use the analysis flag
ANL01FL
to select the records that should be used as part
of the analysis based on RECIST 1.1. Subsequently we then build on this
analysis flag (see ANLzzFL
) to
demonstrate additional analysis flags for example purposes only. The
analysis flags therefore should be setup according to your company
specifications.
In the example below, the lesion sizes are counted and grouped
according to the by variables specified, in this case we group by the
visit (AVISIT
and AVISITN
) to ensure that the
correct lesion measurements are summed together. If there are different
analysis day/dates (ADY
/ADT
) associated with a
particular visit, we take the minimum day/date.
adtr_sum <- derive_summary_records(
dataset_add = adtr,
by_vars = c(get_admiral_option("subject_keys"), adsl_vars, exprs(AVISIT, AVISITN)),
filter_add = (str_starts(PARAMCD, "LDIAM") & TULOCGR1 == "NON-NODAL") |
(str_starts(PARAMCD, "NLDIAM") & TULOCGR1 == "NODAL"),
set_values_to = exprs(
AVAL = sum(AVAL, na.rm = TRUE),
ADY = min(ADY, na.rm = TRUE),
ADT = min(ADT, na.rm = TRUE),
PARAMCD = "SDIAM",
PARAM = "Target Lesions Sum of Diameters by Investigator",
PARCAT1 = "Target Lesion(s)",
PARCAT2 = "Investigator",
PARCAT3 = "RECIST 1.1"
)
)
USUBJID | PARAMCD | PARAM | AVISIT | AVAL | ADT | ADY |
---|---|---|---|---|---|---|
01-701-1015 | SDIAM | Target Lesions Sum of Diameters by Investigator | BASELINE | 96 | 2014-01-02 | 1 |
01-701-1015 | SDIAM | Target Lesions Sum of Diameters by Investigator | WEEK 3 | 96 | 2014-01-23 | 22 |
01-701-1015 | SDIAM | Target Lesions Sum of Diameters by Investigator | WEEK 6 | 38 | 2014-02-01 | 31 |
01-701-1015 | SDIAM | Target Lesions Sum of Diameters by Investigator | WEEK 9 | 7 | 2014-03-06 | 64 |
01-701-1028 | SDIAM | Target Lesions Sum of Diameters by Investigator | BASELINE | 94 | 2013-07-19 | 1 |
01-701-1028 | SDIAM | Target Lesions Sum of Diameters by Investigator | WEEK 3 | 91 | 2013-08-09 | 22 |
01-701-1028 | SDIAM | Target Lesions Sum of Diameters by Investigator | WEEK 6 | 110 | 2013-08-30 | 43 |
01-701-1028 | SDIAM | Target Lesions Sum of Diameters by Investigator | WEEK 9 | 92 | 2013-09-20 | 64 |
01-701-1115 | SDIAM | Target Lesions Sum of Diameters by Investigator | BASELINE | 90 | 2012-11-30 | 1 |
01-701-1115 | SDIAM | Target Lesions Sum of Diameters by Investigator | WEEK 3 | 74 | 2012-12-21 | 22 |
The analysis flag ANL01FL
flags the sum of diameters
where the number of lesions assessed at baseline and at post-baseline
match. To assess whether the number of lesions expected and assessed
match, you could compare lesion identifiers (TR.TRLNKID
) or
use the previous variables set up earlier to compare lesion expected
(LSEXP
) vs lesion assessed (LSASS
) or check
for target lesion response (from RS
) = NE
. A
target lesion response of NE
means all lesions from
baseline are not measured at that post-baseline visit.
Lesions can split or merge, if this is a consideration then this needs to be taken care of when deciding on which algorithm to use to check that all lesions are measured at post-baseline.
In the below example, we use derive_var_merged_summary()
function multiple times in order to process additional variables for sum
of diameter parameter, and calculate the analysis flag
(ANL01FL
).
In the first step we concatenate lesions IDs measured
LSEXP
at baseline, and merge in with the second step where
we concatenate the lesions IDs assessed LSASS
at that visit
record. You can then compare LSEXP
vs LSASS
at
each visit.
In the final step, the analysis flag is set
ANL01FL = "Y"
where lesions assessed at post-baseline match
baseline.
adtr_sum <- adtr_sum %>%
derive_var_merged_summary(
dataset_add = adtr,
by_vars = get_admiral_option("subject_keys"),
filter_add = AVISIT == "BASELINE" &
((str_starts(PARAMCD, "LDIAM") & TULOCGR1 == "NON-NODAL") |
(str_starts(PARAMCD, "NLDIAM") & TULOCGR1 == "NODAL")),
new_vars = exprs(LSEXP = paste(sort(TRLNKID), collapse = ", "))
) %>%
derive_var_merged_summary(
dataset_add = adtr,
by_vars = c(get_admiral_option("subject_keys"), exprs(AVISIT)),
filter_add = ((str_starts(PARAMCD, "LDIAM") & TULOCGR1 == "NON-NODAL") |
(str_starts(PARAMCD, "NLDIAM") & TULOCGR1 == "NODAL")) & ANL01FL == "Y",
new_vars = exprs(LSASS = paste(sort(TRLNKID), collapse = ", "))
) %>%
mutate(
ANL01FL = if_else(LSEXP == LSASS, "Y", NA_character_)
)
ABLFL
, BASE
)These functions are available in admiral
, below examples
show its usage for ADTR
. Here we create baseline record
flag ABLFL
by selecting the last record prior to analysis
day 1, and then derive the baseline value BASE
. If in a
particular study/or subject, baseline is defined differently, you need
to make sure to update appropriate arguments.
NADIR
Once we have Sum of Target Lesions Diameters parameter
(SDIAM
), the NADIR
is set to the lowest sum of
diameters in Analysis Value (AVAL
) before the current
observation, where all lesions were assessed
(i.e. ANL01F == "Y"
). The first observations after baseline
(depending on how you have defined baseline earlier) is set to the
baseline value.
adtr_sum <- adtr_sum %>%
derive_vars_joined(
dataset_add = adtr_sum,
by_vars = get_admiral_option("subject_keys"),
order = exprs(AVAL),
new_vars = exprs(NADIR = AVAL),
join_vars = exprs(ADY),
join_type = "all",
filter_add = ANL01FL == "Y",
filter_join = ADY.join < ADY,
mode = "first",
check_type = "none"
)
USUBJID | PARAMCD | AVISIT | ADY | AVAL | NADIR |
---|---|---|---|---|---|
01-701-1015 | SDIAM | BASELINE | 1 | 96 | NA |
01-701-1015 | SDIAM | WEEK 3 | 22 | 96 | 96 |
01-701-1015 | SDIAM | WEEK 6 | 31 | 38 | 96 |
01-701-1015 | SDIAM | WEEK 9 | 64 | 7 | 96 |
01-701-1028 | SDIAM | BASELINE | 1 | 94 | NA |
01-701-1028 | SDIAM | WEEK 3 | 22 | 91 | 94 |
01-701-1028 | SDIAM | WEEK 6 | 43 | 110 | 91 |
01-701-1028 | SDIAM | WEEK 9 | 64 | 92 | 91 |
01-701-1115 | SDIAM | BASELINE | 1 | 90 | NA |
01-701-1115 | SDIAM | WEEK 3 | 22 | 74 | 90 |
CHG
,
PCHG
)These functions are available in admiral, below examples show its
usage for ADTR
. We calculate the change from baseline
(CHG
, CHGNAD
) and percentage change from
baseline (PCHG
, PCHGNAD
) for the parameter
SDIAM
(using AVAL - BASE
) the variable
NADIR
(using AVAL - NADIR
).
adtr_sum <- adtr_sum %>%
derive_var_chg() %>%
derive_var_pchg() %>%
mutate(
CHGNAD = AVAL - NADIR,
PCHGNAD = if_else(NADIR == 0, NA_real_, 100 * CHGNAD / NADIR)
)
USUBJID | AVISIT | AVAL | NADIR | BASE | CHG | PCHG | CHGNAD | PCHGNAD |
---|---|---|---|---|---|---|---|---|
01-701-1015 | BASELINE | 96 | NA | 96 | 0 | 0.000000 | NA | NA |
01-701-1015 | WEEK 3 | 96 | 96 | 96 | 0 | 0.000000 | 0 | 0.000000 |
01-701-1015 | WEEK 6 | 38 | 96 | 96 | -58 | -60.416667 | -58 | -60.416667 |
01-701-1015 | WEEK 9 | 7 | 96 | 96 | -89 | -92.708333 | -89 | -92.708333 |
01-701-1028 | BASELINE | 94 | NA | 94 | 0 | 0.000000 | NA | NA |
01-701-1028 | WEEK 3 | 91 | 94 | 94 | -3 | -3.191489 | -3 | -3.191489 |
01-701-1028 | WEEK 6 | 110 | 91 | 94 | 16 | 17.021277 | 19 | 20.879121 |
01-701-1028 | WEEK 9 | 92 | 91 | 94 | -2 | -2.127660 | 1 | 1.098901 |
01-701-1115 | BASELINE | 90 | NA | 90 | 0 | 0.000000 | NA | NA |
01-701-1115 | WEEK 3 | 74 | 90 | 90 | -16 | -17.777778 | -16 | -17.777778 |
Depending on the analysis flags you require, you may want to create additional flag variables that will then aid setting up any additional analysis flag variables defined as per your company specifications.
PDFL
In this example, we want to flag when a patient has had
PD
. This could be done in a number of ways:
PD
from ADRS
adtr_sum <- adtr_sum %>%
derive_var_merged_exist_flag(
dataset_add = adrs,
filter_add = PARAMCD == "PD",
by_vars = c(get_admiral_option("subject_keys"), exprs(ADT)),
new_var = PDFL,
condition = AVALC == "Y"
)
USUBJID | AVISIT | AVAL | NADIR | CHGNAD | PCHGNAD | PDFL |
---|---|---|---|---|---|---|
01-701-1015 | BASELINE | 96 | NA | NA | NA | NA |
01-701-1015 | WEEK 3 | 96 | 96 | 0 | 0.000000 | NA |
01-701-1015 | WEEK 6 | 38 | 96 | -58 | -60.416667 | NA |
01-701-1015 | WEEK 9 | 7 | 96 | -89 | -92.708333 | NA |
01-701-1028 | BASELINE | 94 | NA | NA | NA | NA |
01-701-1028 | WEEK 3 | 91 | 94 | -3 | -3.191489 | NA |
01-701-1028 | WEEK 6 | 110 | 91 | 19 | 20.879121 | Y |
01-701-1028 | WEEK 9 | 92 | 91 | 1 | 1.098901 | NA |
01-701-1115 | BASELINE | 90 | NA | NA | NA | NA |
01-701-1115 | WEEK 3 | 74 | 90 | -16 | -17.777778 | NA |
PD
from RS
adtr_sum <- adtr_sum %>%
derive_var_merged_exist_flag(
dataset_add = derive_vars_dt(
rs,
dtc = RSDTC,
new_vars_prefix = "A",
highest_imputation = "D",
flag_imputation = "none"
),
filter_add = RSTESTCD == "OVRLRESP" & RSEVAL == "INVESTIGATOR",
by_vars = c(get_admiral_option("subject_keys"), exprs(ADT)),
new_var = PDFL,
condition = RSSTRESC == "PD"
)
USUBJID | AVISIT | AVAL | NADIR | CHGNAD | PCHGNAD | PDFL |
---|---|---|---|---|---|---|
01-701-1015 | BASELINE | 96 | NA | NA | NA | NA |
01-701-1015 | WEEK 3 | 96 | 96 | 0 | 0.000000 | NA |
01-701-1015 | WEEK 6 | 38 | 96 | -58 | -60.416667 | NA |
01-701-1015 | WEEK 9 | 7 | 96 | -89 | -92.708333 | NA |
01-701-1028 | BASELINE | 94 | NA | NA | NA | NA |
01-701-1028 | WEEK 3 | 91 | 94 | -3 | -3.191489 | NA |
01-701-1028 | WEEK 6 | 110 | 91 | 19 | 20.879121 | Y |
01-701-1028 | WEEK 9 | 92 | 91 | 1 | 1.098901 | NA |
01-701-1115 | BASELINE | 90 | NA | NA | NA | NA |
01-701-1115 | WEEK 3 | 74 | 90 | -16 | -17.777778 | NA |
CR
at the current
observation visit and but a CR
response at the
NADIR
observationadtr_sum <- adtr_sum %>% mutate(
CRFL = if_else(AVAL == 0 & ANL01FL == "Y", "Y", NA_character_),
CRNFL = if_else(NADIR == 0, "Y", NA_character_),
PDFL = if_else(is.na(CRFL) & CRNFL == "Y", "Y", NA_character_)
)
USUBJID | AVISIT | AVAL | NADIR | CHGNAD | PCHGNAD | PDFL |
---|---|---|---|---|---|---|
01-701-1015 | BASELINE | 96 | NA | NA | NA | NA |
01-701-1015 | WEEK 3 | 96 | 96 | 0 | 0.000000 | NA |
01-701-1015 | WEEK 6 | 38 | 96 | -58 | -60.416667 | NA |
01-701-1015 | WEEK 9 | 7 | 96 | -89 | -92.708333 | NA |
01-701-1028 | BASELINE | 94 | NA | NA | NA | NA |
01-701-1028 | WEEK 3 | 91 | 94 | -3 | -3.191489 | NA |
01-701-1028 | WEEK 6 | 110 | 91 | 19 | 20.879121 | NA |
01-701-1028 | WEEK 9 | 92 | 91 | 1 | 1.098901 | NA |
01-701-1115 | BASELINE | 90 | NA | NA | NA | NA |
01-701-1115 | WEEK 3 | 74 | 90 | -16 | -17.777778 | NA |
The derivation for PD
for target lesions is based on the
calculated sum of diameters of the assessed lesions results in a
calculated Percent Change from NADIR >=20% (PCHGNAD
) and
a calculated Change from NADIR >=5mm (CHGNAD
).
In this example, CRFL
flags where the patient has had
Complete Response, and CRNFL
, flags where Complete response
contributed to the NADIR
calculation.
adtr_sum <- adtr_sum %>% mutate(
CRFL = if_else(AVAL == 0 & ANL01FL == "Y", "Y", NA_character_),
CRNFL = if_else(NADIR == 0, "Y", NA_character_)
)
adtr_sum <- adtr_sum %>%
mutate(
PDFL = if_else(
PCHGNAD >= 20 & CHGNAD >= 5 | is.na(CRFL) & CRNFL == "Y",
"Y",
NA_character_
)
)
USUBJID | AVISIT | AVAL | NADIR | CHGNAD | PCHGNAD | PDFL |
---|---|---|---|---|---|---|
01-701-1015 | BASELINE | 96 | NA | NA | NA | NA |
01-701-1015 | WEEK 3 | 96 | 96 | 0 | 0.000000 | NA |
01-701-1015 | WEEK 6 | 38 | 96 | -58 | -60.416667 | NA |
01-701-1015 | WEEK 9 | 7 | 96 | -89 | -92.708333 | NA |
01-701-1028 | BASELINE | 94 | NA | NA | NA | NA |
01-701-1028 | WEEK 3 | 91 | 94 | -3 | -3.191489 | NA |
01-701-1028 | WEEK 6 | 110 | 91 | 19 | 20.879121 | Y |
01-701-1028 | WEEK 9 | 92 | 91 | 1 | 1.098901 | NA |
01-701-1115 | BASELINE | 90 | NA | NA | NA | NA |
01-701-1115 | WEEK 3 | 74 | 90 | -16 | -17.777778 | NA |
ANLzzFL
Based on your specifications, additional analysis flags
(ANLzzFL
) could be set up in ADTR
. Below are
some examples which use the ANL01FL
flag derived earlier
for the sum of diameter parameter (SDIAM
) as a basis.
Analysis 02 Flag (ANL02FL
) only includes visits (where
analysis date is e.g after randomization date (RANDDT
))
with the lowest NADIR
(lowest sum of diameter) so maximum
tumor shrinkage by using the first non-missing lowest percentage change
from baseline (PCHG
).
adtr_sum <- adtr_sum %>%
derive_var_relative_flag(
by_vars = get_admiral_option("subject_keys"),
order = exprs(ADT),
new_var = POSTRNDFL,
condition = ADT > RANDDT,
mode = "first",
selection = "after",
inclusive = TRUE,
flag_no_ref_groups = FALSE
) %>%
restrict_derivation(
derivation = derive_var_extreme_flag,
args = params(
by_vars = get_admiral_option("subject_keys"),
new_var = ANL02FL,
order = exprs(PCHG),
mode = "first",
check_type = "none"
),
filter = ANL01FL == "Y" & POSTRNDFL == "Y"
) %>%
select(-POSTRNDFL)
USUBJID | AVISIT | AVAL | NADIR | PCHG | ANL01FL | ANL02FL |
---|---|---|---|---|---|---|
01-701-1015 | BASELINE | 96 | NA | 0.000000 | Y | NA |
01-701-1015 | WEEK 3 | 96 | 96 | 0.000000 | Y | NA |
01-701-1015 | WEEK 6 | 38 | 96 | -60.416667 | NA | NA |
01-701-1015 | WEEK 9 | 7 | 96 | -92.708333 | Y | Y |
01-701-1028 | BASELINE | 94 | NA | 0.000000 | Y | NA |
01-701-1028 | WEEK 3 | 91 | 94 | -3.191489 | Y | Y |
01-701-1028 | WEEK 6 | 110 | 91 | 17.021277 | NA | NA |
01-701-1028 | WEEK 9 | 92 | 91 | -2.127660 | Y | NA |
01-701-1115 | BASELINE | 90 | NA | 0.000000 | Y | NA |
01-701-1115 | WEEK 3 | 74 | 90 | -17.777778 | Y | NA |
Analysis 03 flag (ANL03FL
) includes all sum of diameters
where all lesions were assessed (where ANL01FL == "Y"
)
until the patient has PD
, indicated by using the
PDFL
variable.
adtr_sum <- adtr_sum %>%
restrict_derivation(
derivation = derive_var_relative_flag,
args = params(
by_vars = get_admiral_option("subject_keys"),
new_var = ANL03FL,
condition = PDFL == "Y",
order = exprs(ADY),
mode = "first",
selection = "before",
inclusive = FALSE
),
filter = ANL01FL == "Y" | PDFL == "Y"
)
USUBJID | AVISIT | AVAL | NADIR | PDFL | ANL01FL | ANL03FL |
---|---|---|---|---|---|---|
01-701-1015 | BASELINE | 96 | NA | NA | Y | Y |
01-701-1015 | WEEK 3 | 96 | 96 | NA | Y | Y |
01-701-1015 | WEEK 6 | 38 | 96 | NA | NA | NA |
01-701-1015 | WEEK 9 | 7 | 96 | NA | Y | Y |
01-701-1028 | BASELINE | 94 | NA | NA | Y | Y |
01-701-1028 | WEEK 3 | 91 | 94 | NA | Y | Y |
01-701-1028 | WEEK 6 | 110 | 91 | Y | NA | NA |
01-701-1028 | WEEK 9 | 92 | 91 | NA | Y | NA |
01-701-1115 | BASELINE | 90 | NA | NA | Y | Y |
01-701-1115 | WEEK 3 | 74 | 90 | NA | Y | Y |
Analysis 04 flag (ANL04FL
) includes all sum of diameters
where all lesions were assessed (where ANL01FL == "Y"
) and
additionally those indicating PD
using the
PDFL
variable.
adtr_sum <- adtr_sum %>%
mutate(
ANL04FL = if_else(ANL01FL == "Y" | PDFL == "Y", "Y", NA_character_)
)
USUBJID | AVISIT | AVAL | NADIR | PDFL | ANL01FL | ANL04FL |
---|---|---|---|---|---|---|
01-701-1015 | BASELINE | 96 | NA | NA | Y | Y |
01-701-1015 | WEEK 3 | 96 | 96 | NA | Y | Y |
01-701-1015 | WEEK 6 | 38 | 96 | NA | NA | NA |
01-701-1015 | WEEK 9 | 7 | 96 | NA | Y | Y |
01-701-1028 | BASELINE | 94 | NA | NA | Y | Y |
01-701-1028 | WEEK 3 | 91 | 94 | NA | Y | Y |
01-701-1028 | WEEK 6 | 110 | 91 | Y | NA | Y |
01-701-1028 | WEEK 9 | 92 | 91 | NA | Y | Y |
01-701-1115 | BASELINE | 90 | NA | NA | Y | Y |
01-701-1115 | WEEK 3 | 74 | 90 | NA | Y | Y |
ASEQ
)The {admiral}
function
admiral::derive_var_obs_number()
can be used to derive
ASEQ
:
adtr <- adtr %>%
derive_var_obs_number(
by_vars = get_admiral_option("subject_keys"),
order = exprs(PARAMCD, AVISITN, TRSEQ),
check_type = "error"
)
USUBJID | PARAMCD | AVISIT | ASEQ | AVAL |
---|---|---|---|---|
01-701-1015 | LDIAM1 | BASELINE | 1 | 21.00 |
01-701-1015 | LDIAM1 | WEEK 3 | 2 | 20.00 |
01-701-1015 | LDIAM1 | WEEK 6 | 3 | 20.00 |
01-701-1015 | LDIAM1 | WEEK 9 | 4 | 0.00 |
01-701-1015 | LDIAM2 | BASELINE | 5 | 33.28 |
01-701-1015 | LDIAM2 | WEEK 3 | 6 | 35.70 |
01-701-1015 | LDIAM2 | WEEK 9 | 7 | 7.49 |
01-701-1015 | LDIAM3 | BASELINE | 8 | 24.00 |
01-701-1015 | LDIAM3 | WEEK 3 | 9 | 24.00 |
01-701-1015 | LDIAM3 | WEEK 9 | 10 | 0.00 |
ADSL
VariablesIf needed, the other ADSL
variables can now be added.
List of ADSL
variables already merged held in vector
adsl_vars
.
adtr <- adtr %>%
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 |
---|---|
ADTR |
admiral::use_ad_template("ADTR", package = "admiralonco") |
TR
with
TU
and Derive New VariablesLDIAMn
&
NLDIAMn
)ANL01FL
ABLFL
,
BASE
)NADIR
CHG
, PCHG
)ANLzzFL
ASEQ
)ADSL
Variables