This article describes creating an ADTTE
(time-to-event)
ADaM with common oncology endpoint parameters.
The main part in programming a time-to-event dataset is the
definition of the events and censoring times.
{admiral}
/{admiralonco}
supports single events
like death (Overall Survival) or composite events like disease
progression or death (Progression Free Survival). More than one source
dataset can be used for the definition of the event and censoring
times.
The majority of the functions used here exist from
{admiral}
, except for the tte_sources
helper
object, provided as an example from {admiralonco}
. In
practice, each company would create their own version of this, as likely
the exact specifications such as filtering condition or description
metadata will vary.
Note: All examples assume CDISC SDTM and/or ADaM format as input unless otherwise specified.
CNSR
,
ADT
, STARTDT
)AVAL
)ASEQ
)To start, all datasets needed for the creation of the time-to-event dataset should be read into the environment. This will be a company specific process.
For example purpose, the ADaM datasets—which are included in
{pharmaverseadam}
—are used. An alternative might be to use
ADEVENT
as input.
CNSR
, ADT
,
STARTDT
)To derive the parameter dependent variables like CNSR
,
ADT
, STARTDT
, EVNTDESC
,
SRCDOM
, PARAMCD
, … the
admiral::derive_param_tte()
function can be used. It adds
one parameter to the input dataset with one observation per subject.
Usually it is called several times.
For each subject it is determined if an event occurred. In the
affirmative the analysis date ADT
is set to the earliest
event date. If no event occurred, the analysis date is set to the latest
censoring date.
The events and censorings are defined by the
admiral::event_source()
and the
admiral::censor_source()
class respectively. It defines
filter
parameter) of a source
dataset (dataset_name
parameter) are potential events or
censorings,CNSR
variable (censor
parameter), anddate
parameter).The date can be provided as date (--DT
variable),
datetime (--DTM
variable), or character ISO-8601 date
(--DTC
variable).
CDISC strongly recommends CNSR = 0
for events and
positive integers for censorings.
{admiral}
/{admiralonco}
enforce this
recommendation. Therefore the censor
parameter is available
for admiral::censor_source()
only. It is defaulted to
1
.
The dataset_name
parameter expects a character value
which is used as an identifier. The actual data which is used for the
derivation of the parameter is provided via the
source_datasets
parameter of
admiral::derive_param_tte()
. It expects a named list of
datasets. The names correspond to the identifiers specified for the
dataset_name
parameter. This allows to define events and
censoring independent of the data.
The table below shows all pre-defined tte_source
objects
which should cover the most common oncology use cases.
object | dataset_name | filter | date | censor | set_values_to |
---|---|---|---|---|---|
lastalive_censor | adsl | NULL | LSTALVDT | 1 | EVNTDESC: “Alive” CNSDTDSC: “Alive During Study” SRCDOM: “ADSL” SRCVAR: “LSTALVDT” |
trts_censor | adsl | NULL | TRTSDT | 1 | EVNTDESC: “Treatment Start” CNSDTDSC: “Treatment Start” SRCDOM: “ADSL” SRCVAR: “TRTSDT” |
pd_event | adrs | PARAMCD == “PD” & AVALC == “Y” & ANL01FL == “Y” | ADT | 0 | EVNTDESC: “Disease Progression” SRCDOM: “ADRS” SRCVAR: “ADT” SRCSEQ: ASEQ |
death_event | adrs | PARAMCD == “DEATH” & AVALC == “Y” & ANL01FL == “Y” | ADT | 0 | EVNTDESC: “Death” SRCDOM: “ADRS” SRCVAR: “ADT” SRCSEQ: ASEQ |
lasta_censor | adrs | PARAMCD == “LSTA” & ANL01FL == “Y” | ADT | 1 | EVNTDESC: “Last Tumor Assessment” CNSDTDSC: “Last Tumor Assessment” SRCDOM: “ADRS” SRCVAR: “ADT” SRCSEQ: ASEQ |
rand_censor | adsl | NULL | RANDDT | 1 | EVNTDESC: “Randomization” CNSDTDSC: “Randomization” SRCDOM: “ADSL” SRCVAR: “RANDDT” |
As mentioned in the introduction, each company would create their own
version of this with the required filtering conditions and metadata as
per your company approach. An example of a possible different approach
could be as follows, where death is sourced from ADSL
,
instead of ADRS
, and the given EVNTDESC
is
different.
adsl_death_event <- event_source(
dataset_name = "adsl",
date = DTHDT,
set_values_to = exprs(
EVNTDESC = "STUDY DEATH",
SRCDOM = "ADSL",
SRCVAR = "DTHDT"
)
)
An optional step at this stage would be required to enable derivation
of duration of response: If using ADRS
/
ADEVENT
parameters as input for any response dates (instead
of a variable in ADSL
) then you would need to use
admiral::derive_vars_merged()
to add the response date as a
temporary variable (e.g. TEMP_RESPDT
) to be able to feed
into admiral::derive_param_tte()
as the start date. You
would also need to use this to filter the source ADSL
dataset so as to only derive the records for responders. This could also
be repeated as needed for IRF/BICR and confirmed responses.
Here is an example of the code needed.
adsl <- adsl %>%
derive_vars_merged(
dataset_add = adrs,
filter_add = PARAMCD == "RSP" & AVALC == "Y" & ANL01FL == "Y",
by_vars = get_admiral_option("subject_keys"),
new_vars = exprs(TEMP_RESPDT = ADT)
)
The pre-defined objects can be passed directly to
admiral::derive_param_tte()
to create a new time-to-event
parameter. Below shows example calls for Overall Survival (OS),
Progression Free Survival (PFS), and duration of response (as above,
this is only derived for responder patients so we have to filter source
ADSL
dataset). Note that the reason for including a
randomization date censor is to catch those patients that never have a
tumor assessment.
adtte <- derive_param_tte(
dataset_adsl = adsl,
start_date = RANDDT,
event_conditions = list(death_event),
censor_conditions = list(lastalive_censor, rand_censor),
source_datasets = list(adsl = adsl, adrs = adrs),
set_values_to = exprs(PARAMCD = "OS", PARAM = "Overall Survival")
) %>%
derive_param_tte(
dataset_adsl = adsl,
start_date = RANDDT,
event_conditions = list(pd_event, death_event),
censor_conditions = list(lasta_censor, rand_censor),
source_datasets = list(adsl = adsl, adrs = adrs),
set_values_to = exprs(PARAMCD = "PFS", PARAM = "Progression Free Survival")
) %>%
derive_param_tte(
dataset_adsl = filter(adsl, !is.na(TEMP_RESPDT)),
start_date = TEMP_RESPDT,
event_conditions = list(pd_event, death_event),
censor_conditions = list(lasta_censor),
source_datasets = list(adsl = adsl, adrs = adrs),
set_values_to = exprs(PARAMCD = "RSD", PARAM = "Duration of Response")
)
USUBJID | PARAMCD | PARAM | STARTDT | ADT | CNSR |
---|---|---|---|---|---|
01-701-1015 | OS | Overall Survival | 2014-01-02 | 2014-07-02 | 1 |
01-701-1023 | OS | Overall Survival | 2012-08-05 | 2012-09-02 | 1 |
01-701-1028 | OS | Overall Survival | 2013-07-19 | 2014-01-14 | 1 |
01-701-1033 | OS | Overall Survival | 2014-03-18 | 2014-04-14 | 1 |
01-701-1034 | OS | Overall Survival | 2014-07-01 | 2014-12-30 | 1 |
01-701-1047 | OS | Overall Survival | 2013-02-12 | 2013-04-07 | 1 |
01-701-1097 | OS | Overall Survival | 2014-01-01 | 2014-07-09 | 1 |
01-701-1111 | OS | Overall Survival | 2012-09-07 | 2012-09-17 | 1 |
01-701-1115 | OS | Overall Survival | 2012-11-30 | 2013-01-23 | 1 |
01-701-1118 | OS | Overall Survival | 2014-03-12 | 2014-09-09 | 1 |
We advise you consult the {admiral}
Creating
a BDS Time-to-Event ADaM vignette for further guidance on the
different options available and more examples.
One extra common oncology case we include here is around PFS when
censoring at new anti-cancer therapy. This could either be controlled
using ANLzzFL
as explained in the ADRS vignette, so that
records after new anti-cancer therapy never contribute to the PD and
DEATH parameters. Or alternatively you can control this on the ADTTE
side by filtering which records are used in
admiral::event_source()
and
admiral::censor_source()
, e.g. for PD or death event date
we can use filter
argument to exclude events occurring
after new anti-cancer therapy.
The censor could be set as whichever date your analysis requires,
e.g. date of last tumor assessment prior to new anti-cancer therapy or
last radiological assessment. If you pass multiple censor dates then
remember the function will choose the latest occurring of these, so be
cautious here if feeding in say one censor date for last assessment
prior to new anti-cancer therapy and one for last assessment - as the
function would choose the maximum of these which in this case would be
incorrect. The easiest solution here would be to pass in one censor date
as the date of last assessment prior to new anti-cancer therapy or date
of last assessment if no new anti-cancer therapy. If
you wanted to use different censor dates which could have different
CNSDTDSC
values, then you’d need to ensure only one is set
per patient.
This case is demonstrated in the below example (where
NACTDT
would be pre-derived as first date of new
anti-cancer therapy, and LASTANDT
as the single tumor
assessment censor date as described above. See
{admiralonco}
Creating and Using New
Anti-Cancer Start Date for deriving NACTDT
).
pd_nact_event <- event_source(
dataset_name = "adsl",
filter = PDDT < NACTDT | is.na(NACTDT),
date = PDDT,
set_values_to = exprs(
EVNTDESC = "Disease Progression prior to NACT",
SRCDOM = "ADSL",
SRCVAR = "PDDT"
)
)
death_nact_event <- event_source(
dataset_name = "adsl",
filter = DTHDT < NACTDT | is.na(NACTDT),
date = DTHDT,
set_values_to = exprs(
EVNTDESC = "Death prior to NACT",
SRCDOM = "ADSL",
SRCVAR = "DTHDT"
)
)
lasta_nact_censor <- censor_source(
dataset_name = "adsl",
date = LASTANDT,
set_values_to = exprs(
EVNTDESC = "Last Tumor Assessment prior to NACT",
CNSDTDSC = "Last Tumor Assessment prior to NACT",
SRCDOM = "ADSL",
SRCVAR = "LASTANDT"
)
)
adtte <- derive_param_tte(
dataset_adsl = adsl,
start_date = RANDDT,
event_conditions = list(pd_nact_event, death_nact_event),
censor_conditions = list(lasta_nact_censor, rand_censor),
source_datasets = list(adsl = adsl),
set_values_to = exprs(PARAMCD = "PFSNACT", PARAM = "Progression Free Survival prior to NACT")
)
AVAL
)The analysis value (AVAL
) can be derived by calling
admiral::derive_vars_duration()
.
This example derives the time to event in days.
STUDYID | USUBJID | ADT | EVNTDESC | SRCDOM | SRCVAR | SRCSEQ | CNSR | CNSDTDSC | STARTDT | PARAMCD | PARAM | AVAL |
---|---|---|---|---|---|---|---|---|---|---|---|---|
CDISCPILOT01 | 01-701-1015 | 2014-07-02 | Alive | ADSL | LSTALVDT | NA | 1 | Alive During Study | 2014-01-02 | OS | Overall Survival | 182 |
CDISCPILOT01 | 01-701-1023 | 2012-09-02 | Alive | ADSL | LSTALVDT | NA | 1 | Alive During Study | 2012-08-05 | OS | Overall Survival | 29 |
CDISCPILOT01 | 01-701-1028 | 2014-01-14 | Alive | ADSL | LSTALVDT | NA | 1 | Alive During Study | 2013-07-19 | OS | Overall Survival | 180 |
CDISCPILOT01 | 01-701-1033 | 2014-04-14 | Alive | ADSL | LSTALVDT | NA | 1 | Alive During Study | 2014-03-18 | OS | Overall Survival | 28 |
CDISCPILOT01 | 01-701-1034 | 2014-12-30 | Alive | ADSL | LSTALVDT | NA | 1 | Alive During Study | 2014-07-01 | OS | Overall Survival | 183 |
CDISCPILOT01 | 01-701-1047 | 2013-04-07 | Alive | ADSL | LSTALVDT | NA | 1 | Alive During Study | 2013-02-12 | OS | Overall Survival | 55 |
CDISCPILOT01 | 01-701-1097 | 2014-07-09 | Alive | ADSL | LSTALVDT | NA | 1 | Alive During Study | 2014-01-01 | OS | Overall Survival | 190 |
CDISCPILOT01 | 01-701-1111 | 2012-09-17 | Alive | ADSL | LSTALVDT | NA | 1 | Alive During Study | 2012-09-07 | OS | Overall Survival | 11 |
CDISCPILOT01 | 01-701-1115 | 2013-01-23 | Alive | ADSL | LSTALVDT | NA | 1 | Alive During Study | 2012-11-30 | OS | Overall Survival | 55 |
CDISCPILOT01 | 01-701-1118 | 2014-09-09 | Alive | ADSL | LSTALVDT | NA | 1 | Alive During Study | 2014-03-12 | OS | Overall Survival | 182 |
Other time units, such as months that we commonly see in oncology
analyses, can be requested by specifying the out_unit
parameter. See the example below. Note that because of the underlying
lubridate::time_length()
function that is used here this
may perform slightly differently to your expectations, e.g. both
time_length(ymd("2021-01-01") %--% ymd("2021-02-01"), "month")
and
time_length(ymd("2021-02-01") %--% ymd("2021-03-01"), "month")
results in exactly 1 month, which is a logical approach but it gives a
different result to the convention of assuming every month has exactly
equal days and just using /30.4375
here or some other such
convention. The difference would only be noticed for small durations,
but if the user prefers an alternative approach they could calculate in
the default days and then add extra processing to convert to months with
their company-specific convention.
ASEQ
)The {admiral}
function
admiral::derive_var_obs_number()
can be used to derive
ASEQ
:
adtte <- adtte %>%
derive_var_obs_number(
by_vars = get_admiral_option("subject_keys"),
order = exprs(PARAMCD),
check_type = "error"
)
STUDYID | USUBJID | ADT | EVNTDESC | SRCDOM | SRCVAR | SRCSEQ | CNSR | CNSDTDSC | STARTDT | PARAMCD | PARAM | AVAL | ASEQ |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
CDISCPILOT01 | 01-701-1015 | 2014-07-02 | Alive | ADSL | LSTALVDT | NA | 1 | Alive During Study | 2014-01-02 | OS | Overall Survival | 182 | 1 |
CDISCPILOT01 | 01-701-1015 | 2014-03-06 | Last Tumor Assessment | ADRS | ADT | 9 | 1 | Last Tumor Assessment | 2014-01-02 | PFS | Progression Free Survival | 64 | 2 |
CDISCPILOT01 | 01-701-1015 | 2014-03-06 | Last Tumor Assessment | ADRS | ADT | 9 | 1 | Last Tumor Assessment | 2014-03-06 | RSD | Duration of Response | 1 | 3 |
CDISCPILOT01 | 01-701-1023 | 2012-09-02 | Alive | ADSL | LSTALVDT | NA | 1 | Alive During Study | 2012-08-05 | OS | Overall Survival | 29 | 1 |
CDISCPILOT01 | 01-701-1023 | 2012-08-05 | Randomization | ADSL | RANDDT | NA | 1 | Randomization | 2012-08-05 | PFS | Progression Free Survival | 1 | 2 |
CDISCPILOT01 | 01-701-1028 | 2014-01-14 | Alive | ADSL | LSTALVDT | NA | 1 | Alive During Study | 2013-07-19 | OS | Overall Survival | 180 | 1 |
CDISCPILOT01 | 01-701-1028 | 2013-08-30 | Disease Progression | ADRS | ADT | 14 | 0 | NA | 2013-07-19 | PFS | Progression Free Survival | 43 | 2 |
CDISCPILOT01 | 01-701-1033 | 2014-04-14 | Alive | ADSL | LSTALVDT | NA | 1 | Alive During Study | 2014-03-18 | OS | Overall Survival | 28 | 1 |
CDISCPILOT01 | 01-701-1033 | 2014-03-18 | Randomization | ADSL | RANDDT | NA | 1 | Randomization | 2014-03-18 | PFS | Progression Free Survival | 1 | 2 |
CDISCPILOT01 | 01-701-1034 | 2014-12-30 | Alive | ADSL | LSTALVDT | NA | 1 | Alive During Study | 2014-07-01 | OS | Overall Survival | 183 | 1 |
Variables from ADSL which are required for time-to-event analyses,
e.g., treatment variables or covariates can be added using
admiral::derive_vars_merged()
.
adtte <- adtte %>%
derive_vars_merged(
dataset_add = adsl,
new_vars = exprs(ARMCD, ARM, ACTARMCD, ACTARM, AGE, SEX),
by_vars = get_admiral_option("subject_keys")
)
USUBJID | PARAMCD | CNSR | AVAL | ARMCD | AGE | SEX |
---|---|---|---|---|---|---|
01-701-1015 | OS | 1 | 182 | Pbo | 63 | F |
01-701-1015 | PFS | 1 | 64 | Pbo | 63 | F |
01-701-1015 | RSD | 1 | 1 | Pbo | 63 | F |
01-701-1023 | OS | 1 | 29 | Pbo | 64 | M |
01-701-1023 | PFS | 1 | 1 | Pbo | 64 | M |
01-701-1028 | OS | 1 | 180 | Xan_Hi | 71 | M |
01-701-1028 | PFS | 0 | 43 | Xan_Hi | 71 | M |
01-701-1033 | OS | 1 | 28 | Xan_Lo | 74 | M |
01-701-1033 | PFS | 1 | 1 | Xan_Lo | 74 | M |
01-701-1034 | OS | 1 | 183 | Xan_Hi | 77 | F |
ADaM | Sample Code |
---|---|
ADTTE | admiral::use_ad_template("ADTTE", package = "admiralonco") |