Derive a Time-to-Event Parameter

Add a time-to-event parameter to the input dataset.

Usage

derive_param_tte(
  dataset = NULL,
  dataset_adsl,
  source_datasets,
  by_vars = NULL,
  start_date = TRTSDT,
  end_dates = NULL,
  event_conditions,
  censor_conditions = NULL,
  event_type = "negative",
  create_datetime = FALSE,
  set_values_to,
  subject_keys = get_admiral_option("subject_keys"),
  check_type = "warning"
)

Arguments

dataset

Input dataset

PARAMCD is expected.

Permitted values: a dataset, i.e., a data.frame or tibble
Default value: NULL

dataset_adsl

ADSL input dataset

The variables specified for start_date, and subject_keys are expected.

Permitted values: a dataset, i.e., a data.frame or tibble
Default value: none

source_datasets

Source datasets

A named list of datasets is expected. The dataset_name field of tte_source() refers to the dataset provided in the list.

Permitted values: named list of datasets, e.g., list(adsl = adsl, ae = ae)
Default value: none

by_vars

By variables

If the parameter is specified, separate time to event parameters are derived for each by group.

The by variables must be in at least one of the source datasets. Each source dataset must contain either all by variables or none of the by variables.

The by variables are not included in the output dataset.

Permitted values: list of variables created by exprs(), e.g., exprs(USUBJID, VISIT)
Default value: NULL

start_date

Time to event origin date

The variable STARTDT is set to the specified date. The value is taken from the ADSL dataset.

If the event or censoring date is before the origin date, ADT is set to the origin date.

Permitted values: a date or datetime variable
Default value: TRTSDT

end_dates

Time to event end date(s)

A list of censor_source() objects is expected. Each date restricts the observation period for time-to-event analysis. For each subject the earliest date across all end_dates is used as the end date for that subject. The records defined by event_conditions and censor_conditions are restricted to dates before or equal to the selected end date.

This argument should be specified if there is more than one reason for stopping the observation of a subject, e.g., end of study, death, or start of new drug. If there is only one reason for stopping the observation, it is sufficient to just include this as a censoring condition in censor_conditions.

Permitted values: a list of source objects, e.g., list(pd, death)
Default value: NULL

event_conditions

Sources and conditions defining events

A list of event_source() objects is expected.

Permitted values: a list of source objects, e.g., list(pd, death)
Default value: none

censor_conditions

Sources and conditions defining censorings

A list of censor_source() objects is expected. Each records defined by the censor_conditions should be a possible censoring time, i.e., at this time there should be known that the event of interest has not yet occurred. Assessment were it is not known whether the event of interest has occurred or not should not be included as censoring times, e.g., when an assessment was not evaluable.

It is acceptable to include records with event as long as these are also included in event_conditions because event take precedence over censorings.

Permitted values: a list of source objects, e.g., list(pd, death)
Default value: NULL

event_type

Type of event

For events that are considered unfavorable, e.g., adverse events, progression, worsening, etc., the value should be "negative" and for events that are considered favorable, e.g., response to treatment, improvement, etc., the value should be "positive".

If event_type is specified as "positive", the objects specified for end_dates are added to the censoring conditions (censor_conditions). I.e., if a subject is censored, it is censored at the earliest date provided by end_dates.

Permitted values: "negative", "positive"
Default value: "negative"

create_datetime

Create datetime variables?

If set to TRUE, variables ADTM and STARTDTM are created. Otherwise, variables ADT and STARTDT are created.

Permitted values: TRUE, FALSE
Default value: FALSE

set_values_to

Variables to set

A named list returned by exprs() defining the variables to be set for the new parameter, e.g. exprs(PARAMCD = "OS", PARAM = "Overall Survival") is expected. The values must be symbols, character strings, numeric values, expressions, or NA.

Permitted values: list of named expressions created by a formula using exprs(), e.g., exprs(AVALC = VSSTRESC, AVAL = yn_to_numeric(AVALC))
Default value: none

subject_keys

Variables to uniquely identify a subject

A list of symbols created using exprs() is expected.

Permitted values: list of variables created by exprs(), e.g., exprs(USUBJID, VISIT)
Default value: get_admiral_option("subject_keys")

check_type

Check uniqueness

If "warning", "message", or "error" is specified, the specified message is issued if the observations of the source datasets are not unique with respect to the by variables and the date and order specified in the event_source() and censor_source() objects.

Permitted values: "none", "message", "warning", "error"
Default value: "warning"

Value

The input dataset with the new parameter added

Details

The following steps are performed to create the observations of the new parameter:

Deriving the events:

For each event source dataset the observations as specified by the filter element are selected. Then for each subject the first observation (with respect to date and order) is selected.
The ADT variable is set to the variable specified by the date element. If the date variable is a datetime variable, only the datepart is copied.
The CNSR variable is added and set to the censor element.
The variables specified by the set_values_to element are added.
The selected observations of all event source datasets are combined into a single dataset.
For each subject the first observation (with respect to the ADT/ADTM variable) from the single dataset is selected. If there is more than one event with the same date, the first event with respect to the order of events in event_conditions is selected.

Deriving the censoring observations:

For each censoring source dataset the observations as specified by the filter element are selected. Then for each subject the last observation (with respect to date and order) is selected.
The ADT variable is set to the variable specified by the date element. If the date variable is a datetime variable, only the datepart is copied.
The CNSR variable is added and set to the censor element.
The variables specified by the set_values_to element are added.
The selected observations of all censoring source datasets are combined into a single dataset.
For each subject the last observation (with respect to the ADT/ADTM variable) from the single dataset is selected. If there is more than one censoring with the same date, the last censoring with respect to the order of censorings in censor_conditions is selected.

For each subject (as defined by the subject_keys parameter) an observation is selected. If an event is available, the event observation is selected. Otherwise the censoring observation is selected.

Finally:

The variable specified for start_date is joined from the ADSL dataset. Only subjects in both datasets are kept, i.e., subjects with both an event or censoring and an observation in dataset_adsl.
The variables as defined by the set_values_to parameter are added.
The ADT/ADTM variable is set to the maximum of ADT/ADTM and STARTDT/STARTDTM (depending on the create_datetime parameter).
The new observations are added to the output dataset.

Examples

Add a basic time to event parameter

For each subject the time to first adverse event should be created as a parameter.

The event source object is created using event_source() and the date is set to adverse event start date.
The censor source object is created using censor_source() and the date is set to end of study date.
The event and censor source objects are then passed to derive_param_tte() to derive the time to event parameter with the provided parameter descriptions (PARAMCD and PARAM).
Note the values of the censor variable (CNSR) that are derived below, where the first subject has an event and the second does not.

library(tibble)
library(dplyr, warn.conflicts = FALSE)
library(lubridate, warn.conflicts = FALSE)

adsl <- tribble(
  ~USUBJID, ~TRTSDT,           ~EOSDT,            ~NEWDRGDT,
  "01",     ymd("2020-12-06"), ymd("2021-03-06"), NA,
  "02",     ymd("2021-01-16"), ymd("2021-02-03"), ymd("2021-01-03")
) %>%
  mutate(STUDYID = "AB42")

adae <- tribble(
  ~USUBJID, ~ASTDT,            ~AESEQ, ~AEDECOD,
  "01",     ymd("2021-01-03"),      1, "Flu",
  "01",     ymd("2021-03-04"),      2, "Cough",
  "01",     ymd("2021-03-05"),      3, "Cough"
) %>%
  mutate(STUDYID = "AB42")

ttae <- event_source(
  dataset_name = "adae",
  date = ASTDT,
  set_values_to = exprs(
    EVNTDESC = "AE",
    SRCDOM = "ADAE",
    SRCVAR = "ASTDT",
    SRCSEQ = AESEQ
  )
)

eos <- censor_source(
  dataset_name = "adsl",
  date = EOSDT,
  set_values_to = exprs(
    EVNTDESC = "END OF STUDY",
    SRCDOM = "ADSL",
    SRCVAR = "EOSDT"
  )
)

derive_param_tte(
  dataset_adsl = adsl,
  event_conditions = list(ttae),
  censor_conditions = list(eos),
  source_datasets = list(adsl = adsl, adae = adae),
  set_values_to = exprs(
    PARAMCD = "TTAE",
    PARAM = "Time to First Adverse Event"
  )
) %>%
  select(USUBJID, STARTDT, PARAMCD, PARAM, ADT, CNSR, SRCSEQ)
#> # A tibble: 2 × 7
#>   USUBJID STARTDT    PARAMCD PARAM                       ADT         CNSR SRCSEQ
#>   <chr>   <date>     <chr>   <chr>                       <date>     <int>  <dbl>
#> 1 01      2020-12-06 TTAE    Time to First Adverse Event 2021-01-03     0      1
#> 2 02      2021-01-16 TTAE    Time to First Adverse Event 2021-02-03     1     NA

Adding a by variable (`by_vars`)

By variables can be added using the by_vars argument, e.g., now for each subject the time to first occurrence of each adverse event preferred term (AEDECOD) should be created as parameters.

derive_param_tte(
  dataset_adsl = adsl,
  by_vars = exprs(AEDECOD),
  event_conditions = list(ttae),
  censor_conditions = list(eos),
  source_datasets = list(adsl = adsl, adae = adae),
  set_values_to = exprs(
    PARAMCD = paste0("TTAE", as.numeric(as.factor(AEDECOD))),
    PARAM = paste("Time to First", AEDECOD, "Adverse Event")
  )
) %>%
  select(USUBJID, STARTDT, PARAMCD, PARAM, ADT, CNSR, SRCSEQ)
#> # A tibble: 4 × 7
#>   USUBJID STARTDT    PARAMCD PARAM                       ADT         CNSR SRCSEQ
#>   <chr>   <date>     <chr>   <chr>                       <date>     <int>  <dbl>
#> 1 01      2020-12-06 TTAE1   Time to First Cough Advers… 2021-03-04     0      2
#> 2 01      2020-12-06 TTAE2   Time to First Flu Adverse … 2021-01-03     0      1
#> 3 02      2021-01-16 TTAE1   Time to First Cough Advers… 2021-02-03     1     NA
#> 4 02      2021-01-16 TTAE2   Time to First Flu Adverse … 2021-02-03     1     NA

Handling duplicates (`check_type`)

The source records are checked regarding duplicates with respect to the by variables and the date and order specified in the source objects. By default, a warning is issued if any duplicates are found. Note here how after creating a new adverse event dataset containing a duplicate date for "Cough", it was then passed to the function using the source_datasets argument - where you see below adae = adae_dup.

adae_dup <- tribble(
  ~USUBJID, ~ASTDT,            ~AESEQ, ~AEDECOD, ~AESER,
  "01",     ymd("2021-01-03"),      1, "Flu",    "Y",
  "01",     ymd("2021-03-04"),      2, "Cough",  "N",
  "01",     ymd("2021-03-04"),      3, "Cough",  "Y"
) %>%
  mutate(STUDYID = "AB42")

derive_param_tte(
  dataset_adsl = adsl,
  by_vars = exprs(AEDECOD),
  start_date = TRTSDT,
  source_datasets = list(adsl = adsl, adae = adae_dup),
  event_conditions = list(ttae),
  censor_conditions = list(eos),
  set_values_to = exprs(
    PARAMCD = paste0("TTAE", as.numeric(as.factor(AEDECOD))),
    PARAM = paste("Time to First", AEDECOD, "Adverse Event")
  )
)
#> # A tibble: 4 × 11
#>   USUBJID STUDYID EVNTDESC     SRCDOM SRCVAR SRCSEQ  CNSR ADT        STARTDT
#>   <chr>   <chr>   <chr>        <chr>  <chr>   <dbl> <int> <date>     <date>
#> 1 01      AB42    AE           ADAE   ASTDT       2     0 2021-03-04 2020-12-06
#> 2 01      AB42    AE           ADAE   ASTDT       1     0 2021-01-03 2020-12-06
#> 3 02      AB42    END OF STUDY ADSL   EOSDT      NA     1 2021-02-03 2021-01-16
#> 4 02      AB42    END OF STUDY ADSL   EOSDT      NA     1 2021-02-03 2021-01-16
#> # i 2 more variables: PARAMCD <chr>, PARAM <chr>
#> Warning: Dataset "adae" contains duplicate records with respect to `STUDYID`, `USUBJID`,
#> `AEDECOD`, and `ASTDT`
#> i Run `admiral::get_duplicates_dataset()` to access the duplicate records

For investigating the issue, the dataset of the duplicate source records can be obtained by calling get_duplicates_dataset():

get_duplicates_dataset()
#> Duplicate records with respect to `STUDYID`, `USUBJID`, `AEDECOD`, and `ASTDT`.
#> # A tibble: 2 × 6
#>   STUDYID USUBJID AEDECOD ASTDT      AESEQ AESER
#> * <chr>   <chr>   <chr>   <date>     <dbl> <chr>
#> 1 AB42    01      Cough   2021-03-04     2 N
#> 2 AB42    01      Cough   2021-03-04     3 Y

Common options to solve the issue:

Restricting the source records by specifying/updating the filter argument in the event_source()/censor_source() calls.
Specifying additional variables for order in the event_source()/censor_source() calls.
Setting check_type = "none" in the derive_param_tte() call to ignore any duplicates.

In this example it does not have significant impact which record is chosen as the dates are the same so the time to event derivation will be the same, but it does impact SRCSEQ in the output dataset, so here the second option is used. Note here how you can also define source objects from within the derive_param_tte() function call itself.

derive_param_tte(
  dataset_adsl = adsl,
  by_vars = exprs(AEDECOD),
  start_date = TRTSDT,
  source_datasets = list(adsl = adsl, adae = adae_dup),
  event_conditions = list(event_source(
    dataset_name = "adae",
    date = ASTDT,
    set_values_to = exprs(
      EVNTDESC = "AE",
      SRCDOM = "ADAE",
      SRCVAR = "ASTDT",
      SRCSEQ = AESEQ
    ),
    order = exprs(AESEQ)
  )),
  censor_conditions = list(eos),
  set_values_to = exprs(
    PARAMCD = paste0("TTAE", as.numeric(as.factor(AEDECOD))),
    PARAM = paste("Time to First", AEDECOD, "Adverse Event")
  )
) %>%
  select(USUBJID, STARTDT, PARAMCD, PARAM, ADT, CNSR, SRCSEQ)
#> # A tibble: 4 × 7
#>   USUBJID STARTDT    PARAMCD PARAM                       ADT         CNSR SRCSEQ
#>   <chr>   <date>     <chr>   <chr>                       <date>     <int>  <dbl>
#> 1 01      2020-12-06 TTAE1   Time to First Cough Advers… 2021-03-04     0      2
#> 2 01      2020-12-06 TTAE2   Time to First Flu Adverse … 2021-01-03     0      1
#> 3 02      2021-01-16 TTAE1   Time to First Cough Advers… 2021-02-03     1     NA
#> 4 02      2021-01-16 TTAE2   Time to First Flu Adverse … 2021-02-03     1     NA

Filtering source records (`filter`)

The first option from above could have been achieved using filter, for example here only using serious adverse events.

derive_param_tte(
  dataset_adsl = adsl,
  by_vars = exprs(AEDECOD),
  start_date = TRTSDT,
  source_datasets = list(adsl = adsl, adae = adae_dup),
  event_conditions = list(event_source(
    dataset_name = "adae",
    filter = AESER == "Y",
    date = ASTDT,
    set_values_to = exprs(
      EVNTDESC = "Serious AE",
      SRCDOM = "ADAE",
      SRCVAR = "ASTDT",
      SRCSEQ = AESEQ
    )
  )),
  censor_conditions = list(eos),
  set_values_to = exprs(
    PARAMCD = paste0("TTSAE", as.numeric(as.factor(AEDECOD))),
    PARAM = paste("Time to First Serious", AEDECOD, "Adverse Event")
  )
) %>%
  select(USUBJID, STARTDT, PARAMCD, PARAM, ADT, CNSR, SRCSEQ)
#> # A tibble: 4 × 7
#>   USUBJID STARTDT    PARAMCD PARAM                       ADT         CNSR SRCSEQ
#>   <chr>   <date>     <chr>   <chr>                       <date>     <int>  <dbl>
#> 1 01      2020-12-06 TTSAE1  Time to First Serious Coug… 2021-03-04     0      3
#> 2 01      2020-12-06 TTSAE2  Time to First Serious Flu … 2021-01-03     0      1
#> 3 02      2021-01-16 TTSAE1  Time to First Serious Coug… 2021-02-03     1     NA
#> 4 02      2021-01-16 TTSAE2  Time to First Serious Flu … 2021-02-03     1     NA

Using multiple event/censor conditions (`event_conditions` /`censor_conditions`)

In the above examples, we only have a single event and single censor condition. Here, we now consider multiple conditions for each passed using event_conditions and censor_conditions.

For the event we are going to use first AE and additionally check a lab condition, and for the censor we'll add in treatment start date in case end of study date was ever missing.

adlb <- tribble(
  ~USUBJID, ~ADT,              ~PARAMCD, ~ANRIND,
  "01",     ymd("2020-12-22"), "HGB",    "LOW"
) %>%
  mutate(STUDYID = "AB42")

low_hgb <- event_source(
  dataset_name = "adlb",
  filter = PARAMCD == "HGB" & ANRIND == "LOW",
  date = ADT,
  set_values_to = exprs(
    EVNTDESC = "POSSIBLE ANEMIA",
    SRCDOM = "ADLB",
    SRCVAR = "ADT"
  )
)

trt_start <- censor_source(
  dataset_name = "adsl",
  date = TRTSDT,
  set_values_to = exprs(
    EVNTDESC = "TREATMENT START",
    SRCDOM = "ADSL",
    SRCVAR = "TRTSDT"
  )
)

derive_param_tte(
  dataset_adsl = adsl,
  event_conditions = list(ttae, low_hgb),
  censor_conditions = list(eos, trt_start),
  source_datasets = list(adsl = adsl, adae = adae, adlb = adlb),
  set_values_to = exprs(
    PARAMCD = "TTAELB",
    PARAM = "Time to First Adverse Event or Possible Anemia (Labs)"
  )
) %>%
  select(USUBJID, STARTDT, PARAMCD, PARAM, ADT, CNSR, SRCSEQ)
#> # A tibble: 2 × 7
#>   USUBJID STARTDT    PARAMCD PARAM                       ADT         CNSR SRCSEQ
#>   <chr>   <date>     <chr>   <chr>                       <date>     <int>  <dbl>
#> 1 01      2020-12-06 TTAELB  Time to First Adverse Even… 2020-12-22     0     NA
#> 2 02      2021-01-16 TTAELB  Time to First Adverse Even… 2021-02-03     1     NA

Note above how the earliest event date is always taken and the latest censor date.

Using different censor values (`censor`) and censoring at earliest occurring censor condition

Within censor_source() the value used to denote a censor can be changed from the default of 1.

In this example an extra censor is used for new drug date with the value of 2.

newdrug <- censor_source(
  dataset_name = "adsl",
  date = NEWDRGDT,
  censor = 2,
  set_values_to = exprs(
    EVNTDESC = "NEW DRUG RECEIVED",
    SRCDOM = "ADSL",
    SRCVAR = "NEWDRGDT"
  )
)

derive_param_tte(
  dataset_adsl = adsl,
  by_vars = exprs(AEDECOD),
  event_conditions = list(ttae),
  censor_conditions = list(eos, newdrug),
  source_datasets = list(adsl = adsl, adae = adae),
  set_values_to = exprs(
    PARAMCD = paste0("TTAE", as.numeric(as.factor(AEDECOD))),
    PARAM = paste("Time to First", AEDECOD, "Adverse Event")
  )
) %>%
  select(USUBJID, STARTDT, PARAMCD, PARAM, ADT, CNSR, SRCSEQ)
#> # A tibble: 4 × 7
#>   USUBJID STARTDT    PARAMCD PARAM                       ADT         CNSR SRCSEQ
#>   <chr>   <date>     <chr>   <chr>                       <date>     <int>  <dbl>
#> 1 01      2020-12-06 TTAE1   Time to First Cough Advers… 2021-03-04     0      2
#> 2 01      2020-12-06 TTAE2   Time to First Flu Adverse … 2021-01-03     0      1
#> 3 02      2021-01-16 TTAE1   Time to First Cough Advers… 2021-02-03     1     NA
#> 4 02      2021-01-16 TTAE2   Time to First Flu Adverse … 2021-02-03     1     NA

In this case the results are still the same, because as explained in the above example the latest censor condition is always taken for those without an event. For the second subject this is still the end of study date.

So, if we wanted to instead censor here at the new drug date if subject has one, then we would need to again use the filter argument, but this time for a new end of study censor source object.

eos_nonewdrug <- censor_source(
  dataset_name = "adsl",
  filter = is.na(NEWDRGDT),
  date = EOSDT,
  set_values_to = exprs(
    EVNTDESC = "END OF STUDY",
    SRCDOM = "ADSL",
    SRCVAR = "EOSDT"
  )
)

derive_param_tte(
  dataset_adsl = adsl,
  by_vars = exprs(AEDECOD),
  event_conditions = list(ttae),
  censor_conditions = list(eos_nonewdrug, newdrug),
  source_datasets = list(adsl = adsl, adae = adae),
  set_values_to = exprs(
    PARAMCD = paste0("TTAE", as.numeric(as.factor(AEDECOD))),
    PARAM = paste("Time to First", AEDECOD, "Adverse Event")
  )
) %>%
  select(USUBJID, STARTDT, PARAMCD, PARAM, ADT, CNSR, SRCSEQ)
#> # A tibble: 4 × 7
#>   USUBJID STARTDT    PARAMCD PARAM                       ADT         CNSR SRCSEQ
#>   <chr>   <date>     <chr>   <chr>                       <date>     <int>  <dbl>
#> 1 01      2020-12-06 TTAE1   Time to First Cough Advers… 2021-03-04     0      2
#> 2 01      2020-12-06 TTAE2   Time to First Flu Adverse … 2021-01-03     0      1
#> 3 02      2021-01-16 TTAE1   Time to First Cough Advers… 2021-01-16     2     NA
#> 4 02      2021-01-16 TTAE2   Time to First Flu Adverse … 2021-01-16     2     NA

Overall survival time to event parameter

In oncology trials, this is commonly derived as time from randomization date to death. For those without event, they are censored at the last date they are known to be alive.

The start date is set using start_date argument, now that we need to use different to the default.
In this example, datetime was needed, which can be achieved by setting create_datetime argument to TRUE.

adsl <- tribble(
  ~USUBJID, ~RANDDTM,                       ~LSALVDTM,                      ~DTHDTM,                        ~DTHFL,
  "01",     ymd_hms("2020-10-03 00:00:00"), ymd_hms("2022-12-15 23:59:59"), NA,                             NA,
  "02",     ymd_hms("2021-01-23 00:00:00"), ymd_hms("2021-02-03 19:45:59"), ymd_hms("2021-02-03 19:45:59"), "Y"
) %>%
  mutate(STUDYID = "AB42")

# derive overall survival parameter
death <- event_source(
  dataset_name = "adsl",
  filter = DTHFL == "Y",
  date = DTHDTM,
  set_values_to = exprs(
    EVNTDESC = "DEATH",
    SRCDOM = "ADSL",
    SRCVAR = "DTHDTM"
  )
)

last_alive <- censor_source(
  dataset_name = "adsl",
  date = LSALVDTM,
  set_values_to = exprs(
    EVNTDESC = "LAST DATE KNOWN ALIVE",
    SRCDOM = "ADSL",
    SRCVAR = "LSALVDTM"
  )
)

derive_param_tte(
  dataset_adsl = adsl,
  start_date = RANDDTM,
  event_conditions = list(death),
  censor_conditions = list(last_alive),
  create_datetime = TRUE,
  source_datasets = list(adsl = adsl),
  set_values_to = exprs(
    PARAMCD = "OS",
    PARAM = "Overall Survival"
  )
) %>%
  select(USUBJID, STARTDTM, PARAMCD, PARAM, ADTM, CNSR)
#> # A tibble: 2 × 6
#>   USUBJID STARTDTM            PARAMCD PARAM            ADTM                 CNSR
#>   <chr>   <dttm>              <chr>   <chr>            <dttm>              <int>
#> 1 01      2020-10-03 00:00:00 OS      Overall Survival 2022-12-15 23:59:59     1
#> 2 02      2021-01-23 00:00:00 OS      Overall Survival 2021-02-03 19:45:59     0

Duration of response time to event parameter

In oncology trials, this is commonly derived as time from response until progression or death, or if neither have occurred then censor at last tumor assessment visit date. It is only relevant for subjects with a response. Note how only observations for subjects in dataset_adsl have the new parameter created, so see below how this is filtered only on responders.

adsl_resp <- tribble(
  ~USUBJID, ~DTHFL, ~DTHDT,            ~RSPDT,
  "01",     "Y",    ymd("2021-06-12"), ymd("2021-03-04"),
  "02",     "N",    NA,                NA,
  "03",     "Y",    ymd("2021-08-21"), NA,
  "04",     "N",    NA,                ymd("2021-04-14")
) %>%
  mutate(STUDYID = "AB42")

adrs <- tribble(
  ~USUBJID, ~AVALC, ~ADT,              ~ASEQ,
  "01",     "SD",   ymd("2021-01-03"), 1,
  "01",     "PR",   ymd("2021-03-04"), 2,
  "01",     "PD",   ymd("2021-05-05"), 3,
  "02",     "PD",   ymd("2021-02-03"), 1,
  "04",     "SD",   ymd("2021-02-13"), 1,
  "04",     "PR",   ymd("2021-04-14"), 2,
  "04",     "CR",   ymd("2021-05-15"), 3
) %>%
  mutate(STUDYID = "AB42", PARAMCD = "OVR")

pd <- event_source(
  dataset_name = "adrs",
  filter = AVALC == "PD",
  date = ADT,
  set_values_to = exprs(
    EVENTDESC = "PD",
    SRCDOM = "ADRS",
    SRCVAR = "ADTM",
    SRCSEQ = ASEQ
  )
)

death <- event_source(
  dataset_name = "adsl",
  filter = DTHFL == "Y",
  date = DTHDT,
  set_values_to = exprs(
    EVENTDESC = "DEATH",
    SRCDOM = "ADSL",
    SRCVAR = "DTHDT"
  )
)

last_visit <- censor_source(
  dataset_name = "adrs",
  date = ADT,
  set_values_to = exprs(
    EVENTDESC = "LAST TUMOR ASSESSMENT",
    SRCDOM = "ADRS",
    SRCVAR = "ADTM",
    SRCSEQ = ASEQ
  )
)

derive_param_tte(
  dataset_adsl = filter(adsl_resp, !is.na(RSPDT)),
  start_date = RSPDT,
  event_conditions = list(pd, death),
  censor_conditions = list(last_visit),
  source_datasets = list(adsl = adsl_resp, adrs = adrs),
  set_values_to = exprs(
    PARAMCD = "DURRSP",
    PARAM = "Duration of Response"
  )
) %>%
  select(USUBJID, STARTDT, PARAMCD, PARAM, ADT, CNSR, SRCSEQ)
#> # A tibble: 2 × 7
#>   USUBJID STARTDT    PARAMCD PARAM                ADT         CNSR SRCSEQ
#>   <chr>   <date>     <chr>   <chr>                <date>     <int>  <dbl>
#> 1 01      2021-03-04 DURRSP  Duration of Response 2021-05-05     0      3
#> 2 04      2021-04-14 DURRSP  Duration of Response 2021-05-15     1      3

End of the observation period (`end_dates`)

The end_dates argument can be used to specify the end of the observation period if there is more than one date which restricts the observation period, e.g., end of study date and new drug date. The earliest date is used as the end of the observation period and events/censorings occurring after this date are not considered.

In the example two censor_source() objects are defined, eos and newdrg, for end of study date and new drug date, respectively, and then passed to the end_dates argument.

adsl <- tribble(
  ~USUBJID, ~TRTSDT,           ~EOSDT,            ~NEWDRGDT,
  "01",     ymd("2020-12-06"), ymd("2021-03-06"), NA,
  "02",     ymd("2021-01-16"), ymd("2021-04-03"), ymd("2021-03-21"),
  "03",     ymd("2021-02-01"), NA,                NA,
  "04",     ymd("2021-03-10"), NA,                NA
) %>%
  mutate(STUDYID = "AB42")

adqs <- tribble(
  ~USUBJID, ~ADT,              ~CHG,
  "01",     ymd("2021-01-03"),    5,
  "01",     ymd("2021-02-03"),   -2,
  "01",     ymd("2021-03-01"),   NA,
  "01",     ymd("2021-03-07"),   10,
  "02",     ymd("2021-01-03"),    4,
  "02",     ymd("2021-02-03"),   -1,
  "02",     ymd("2021-04-01"),  -12,
  "03",     ymd("2021-02-15"),    3,
  "03",     ymd("2021-03-15"),  -15
) %>%
  mutate(STUDYID = "AB42")

eos <- censor_source(
  dataset_name = "adsl",
  date = EOSDT,
  censor = 1,
  set_values_to = exprs(
    EVNTDESC = "END OF STUDY"
  )
)

newdrg <- censor_source(
  dataset_name = "adsl",
  date = NEWDRGDT,
  censor = 2,
  set_values_to = exprs(
    EVNTDESC = "NEW DRUG"
  )
)

worsening <- event_source(
  dataset_name = "adqs",
  date = ADT,
  filter = CHG <= -10,
  set_values_to = exprs(
    EVNTDESC = "WORSENING",
    SRCDOM = "ADQS",
    SRCVAR = "ADT"
  )
)

no_worsening <- censor_source(
  dataset_name = "adqs",
  date = ADT,
  filter = !is.na(CHG),
  set_values_to = exprs(
    CNSDTDSC = "LAST ASSESSMENT",
    SRCDOM = "ADQS",
    SRCVAR = "ADT"
  )
)

no_assessment <- censor_source(
  dataset_name = "adsl",
  date = TRTSDT,
  censor = 3,
  consider_end_dates = FALSE,
  set_values_to = exprs(
    EVNTDESC = "NO ASSESSMENTS",
    CNSDTDSC = "TREATMENT START",
    SRCDOM = "ADSL",
    SRCVAR = "TRTSDT"
  )
)

derive_param_tte(
  dataset_adsl = adsl,
  source_datasets = list(adsl = adsl, adqs = adqs),
  start_date = TRTSDT,
  end_dates = list(eos, newdrg),
  event_conditions = list(worsening),
  censor_conditions = list(no_worsening, no_assessment),
  set_values_to = exprs(PARAMCD = "TTWORSE")
) %>%
select(-STUDYID, -PARAMCD)
#> # A tibble: 4 × 8
#>   USUBJID ADT        EVNTDESC        CNSR SRCDOM SRCVAR CNSDTDSC      STARTDT
#>   <chr>   <date>     <chr>          <int> <chr>  <chr>  <chr>         <date>
#> 1 01      2021-02-03 END OF STUDY       1 ADQS   ADT    LAST ASSESSM… 2020-12-06
#> 2 02      2021-02-03 NEW DRUG           2 ADQS   ADT    LAST ASSESSM… 2021-01-16
#> 3 03      2021-03-15 WORSENING          0 ADQS   ADT    <NA>          2021-02-01
#> 4 04      2021-03-10 NO ASSESSMENTS     3 ADSL   TRTSDT TREATMENT ST… 2021-03-10

Please note that CNSR is set to different values depending on the censoring reason:

1 for end of study (eos)
2 for start of a new drug (newdrg)
3 for no ADQS assessments (no_assessments), consider_end_dates = FALSE is specified for this censoring to avoid that the censoring value from the end date (eos and newdrg) is used.

Also note that subject 03 has no event because the assessment with CHG = -12 was excluded as it is after the start of a new drug.

Positive event (`event_type`)

If positive events like response or improvement are analyzed, event_type = "positive" should be used. Subjects without events are censored at the end of the observation period (defined by end_dates) instead of the last assessment. For positive events this is the more conservative approach.

adsl <- tribble(
  ~USUBJID, ~TRTSDT,           ~EOSDT,            ~NEWDRGDT,
  "01",     ymd("2020-12-06"), ymd("2021-03-06"), NA,
  "02",     ymd("2021-01-16"), ymd("2021-04-03"), ymd("2021-03-21"),
  "03",     ymd("2021-02-01"), NA,                NA
) %>%
  mutate(STUDYID = "AB42")

adqs <- tribble(
  ~USUBJID, ~ADT,              ~CHG,
  "01",     ymd("2021-01-03"),    5,
  "01",     ymd("2021-02-03"),   -2,
  "01",     ymd("2021-03-01"),   NA,
  "01",     ymd("2021-03-07"),   10,
  "02",     ymd("2021-01-03"),    4,
  "02",     ymd("2021-02-03"),   -1,
  "02",     ymd("2021-04-01"),  -12,
  "03",     ymd("2021-02-15"),    3,
  "03",     ymd("2021-03-15"),   15
) %>%
  mutate(STUDYID = "AB42")

eos <- censor_source(
  dataset_name = "adsl",
  date = EOSDT,
  set_values_to = exprs(
    EVNTDESC = "END OF STUDY",
    SRCDOM = "ADSL",
    SRCVAR = "EOSDT"
  )
)

newdrg <- censor_source(
  dataset_name = "adsl",
  date = NEWDRGDT,
  set_values_to = exprs(
    EVNTDESC = "NEW DRUG",
    SRCDOM = "ADSL",
    SRCVAR = "NEWDRGDT"
  )
)

improvement <- event_source(
  dataset_name = "adqs",
  date = ADT,
  filter = CHG >= 10,
  set_values_to = exprs(
    EVNTDESC = "IMPROVEMENT",
    SRCDOM = "ADQS",
    SRCVAR = "ADT"
  )
)

no_improvement <- censor_source(
  dataset_name = "adqs",
  date = ADT,
  filter = !is.na(CHG),
  set_values_to = exprs(
    SRCDOM = "ADQS",
    SRCVAR = "ADT"
  )
)

derive_param_tte(
  dataset_adsl = adsl,
  source_datasets = list(adsl = adsl, adqs = adqs),
  start_date = TRTSDT,
  end_dates = list(eos, newdrg),
  event_conditions = list(improvement),
  censor_conditions = list(no_improvement),
  event_type = "positive",
  set_values_to = exprs(PARAMCD = "TTIMPROV")
) %>%
  select(-STUDYID)
#> # A tibble: 3 × 8
#>   USUBJID ADT        EVNTDESC     SRCDOM SRCVAR    CNSR STARTDT    PARAMCD
#>   <chr>   <date>     <chr>        <chr>  <chr>    <int> <date>     <chr>
#> 1 01      2021-03-06 END OF STUDY ADSL   EOSDT        1 2020-12-06 TTIMPROV
#> 2 02      2021-03-21 NEW DRUG     ADSL   NEWDRGDT     1 2021-01-16 TTIMPROV
#> 3 03      2021-03-15 IMPROVEMENT  ADQS   ADT          0 2021-02-01 TTIMPROV

Further examples

Further example usages of this function can be found in the vignette("bds_tte").

Usage

Arguments

Value

Details

See also

Examples

Add a basic time to event parameter

Adding a by variable (by_vars)

Handling duplicates (check_type)

Filtering source records (filter)

Using multiple event/censor conditions (event_conditions /censor_conditions)

Using different censor values (censor) and censoring at earliest occurring censor condition