The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
This vignette is broken into three major sections. The first section
briefly explores the imputation rules used in {admiral}.
The second section focuses on imputation functions that work on vectors
with lots of small examples to explore the imputation rules. These
vector-based functions form the backbone of
{admiral}’s more powerful functions
derive_vars_dt() and derive_vars_dtm() for
building ADaM dataset. The final section moves into more detailed
examples that a user might face while working on ADaMs in need of
---DT and ---DTM variables.
Date and time is collected in SDTM as character values using the
extended ISO 8601
format. For example, "2019-10-9T13:42:00". It allows that
some parts of the date or time are missing, e.g., "2019-10"
if the day and the time is unknown.
The ADaM timing variables like ADTM (Analysis Datetime)
or ADY (Analysis Relative Day) are numeric variables. They
can be derived only if the date or datetime is complete. Therefore
{admiral} provides imputation functions which fill in
missing date or time parts according to certain imputation rules.
In {admiral} users will primarily use two functions
derive_vars_dt() and derive_vars_dtm() for
date and datetime imputations respectively. In all other functions where
dates can be passed as an argument, we expect full dates or datetimes
(unless otherwise specified), so if any possibility of partials then
these functions should be used as a first step to make the required
imputation.
The functions that need to do date/time imputation follow a rule that
we have called Highest Imputation, which has a
corresponding argument in all our functions called
highest_imputation. The rule is best explained by working
through the examples below, but to put it briefly, this rule allows a
user to control which components of the DTC value are imputed if they
are missing.
The default imputation for _dtm() functions,
e.g. impute_dtc_dtm(), derive_vars_dtm(), is
“h” (hours). A user can specify that that no imputation is to be done by
setting highest_imputation = n. However, for for
_dt() functions, e.g. impute_dtc_dt(),
derive_vars_dt() the default imputation is already set as
highest_imputation = "n".
Care must be taken when deciding on level of imputation. If a
component is at a higher level than the highest imputation level is
missing, NA_character_ is returned. For example, for
highest_imputation = "D" "2020" results in
NA_character_ because the month is missing.
We encourage readers to explore in more detail the
highest_imputation options in both the _dtm()
and _dt() function documentations and in the examples
below.
In our first example, we will make use of
impute_dtc_dtm() on 2019-10 setting
highest_imputation = "M". The argument
date_imputation and time_imputation are given
expressed inputs of the imputation we would like to see done.
impute_dtc_dtm(
"2019-10",
highest_imputation = "M",
date_imputation = "01-01",
time_imputation = "00:00:00"
)
#> [1] "2019-10-01T00:00:00"Next we impute using 2019-02, which if done naively can
result in invalid dates, e.g.,
impute_dtc_dtm(
"2019-02",
highest_imputation = "M",
date_imputation = "02-31",
time_imputation = "00:00:00"
)
#> [1] "2019-02-31T00:00:00"Therefore the keywords "first" or "last"
can be specified in date_imputation to request that missing
parts are replaced by the first or last possible value - giving us a
valid date!
impute_dtc_dtm(
"2019-02",
highest_imputation = "M",
date_imputation = "last",
time_imputation = "00:00:00"
)
#> [1] "2019-02-28T00:00:00"For dates, there is the additional option to use keyword
"mid" to impute missing day to 15 or missing
day and month to 06-30, but note the different behavior
below depending on the preserve argument for the case when
month only is missing:
dates <- c(
"2019-02",
"2019",
"2019---01"
)
impute_dtc_dtm(
dates,
highest_imputation = "M",
date_imputation = "mid",
time_imputation = "00:00:00",
preserve = FALSE
)
#> [1] "2019-02-15T00:00:00" "2019-06-30T00:00:00" "2019-06-30T00:00:00"
impute_dtc_dtm(
dates,
highest_imputation = "M",
date_imputation = "mid",
time_imputation = "00:00:00",
preserve = TRUE
)
#> [1] "2019-02-15T00:00:00" "2019-06-30T00:00:00" "2019-06-01T00:00:00"If you wanted to achieve a similar result by replacing any missing
part of the date with a fixed value 06-15, this is also
possible, but note the difference in days for cases when month is
missing:
The imputation level, i.e., which components are imputed if they are
missing, is controlled by the highest_imputation argument.
All components up to the specified level are imputed.
dates <- c(
"2019-02-03T12:30:15",
"2019-02-03T12:30",
"2019-02-03",
"2019-02",
"2019"
)
# Do not impute
impute_dtc_dtm(
dates,
highest_imputation = "n"
)
#> [1] "2019-02-03T12:30:15" NA NA
#> [4] NA NA
# Impute seconds only
impute_dtc_dtm(
dates,
highest_imputation = "s"
)
#> [1] "2019-02-03T12:30:15" "2019-02-03T12:30:00" NA
#> [4] NA NA
# Impute time (hours, minutes, seconds) only
impute_dtc_dtm(
dates,
highest_imputation = "h"
)
#> [1] "2019-02-03T12:30:15" "2019-02-03T12:30:00" "2019-02-03T00:00:00"
#> [4] NA NA
# Impute days and time
impute_dtc_dtm(
dates,
highest_imputation = "D"
)
#> [1] "2019-02-03T12:30:15" "2019-02-03T12:30:00" "2019-02-03T00:00:00"
#> [4] "2019-02-01T00:00:00" NA
# Impute date (months and days) and time
impute_dtc_dtm(
dates,
highest_imputation = "M"
)
#> [1] "2019-02-03T12:30:15" "2019-02-03T12:30:00" "2019-02-03T00:00:00"
#> [4] "2019-02-01T00:00:00" "2019-01-01T00:00:00"For imputation of years (highest_imputation = "Y") see
next section.
In some scenarios the imputed date should not be before or after
certain dates. For example an imputed date after data cut off date or
death date is not desirable. The {admiral} imputation
functions provide the min_dates and max_dates
argument to specify those dates. For example:
impute_dtc_dtm(
"2019-02",
highest_imputation = "M",
date_imputation = "last",
time_imputation = "last",
max_dates = list(ymd("2019-01-14"), ymd("2019-02-25"))
)
#> [1] "2019-02-25T23:59:59"It is ensured that the imputed date is not after any of the specified dates. Only dates which are in the range of possible dates of the DTC value are considered. The possible dates are defined by the missing parts of the DTC date, i.e., for “2019-02” the possible dates range from “2019-02-01” to “2019-02-28”. Thus “2019-01-14” is ignored. This ensures that the non-missing parts of the DTC date are not changed.
If the min_dates or max_dates argument is
specified, it is also possible to impute completely missing dates. For
date_imputation = "first" the min_dates
argument must be specified and for date_imputation = "last"
the max_dates argument. For other imputation rules imputing
the year is not possible.
# Impute year to first
impute_dtc_dtm(
c("2019-02", NA),
highest_imputation = "Y",
min_dates = list(
ymd("2019-01-14", NA),
ymd("2019-02-25", "2020-01-01")
)
)
#> [1] "2019-02-25T00:00:00" "2020-01-01T00:00:00"
# Impute year to last
impute_dtc_dtm(
c("2019-02", NA),
highest_imputation = "Y",
date_imputation = "last",
time_imputation = "last",
max_dates = list(
ymd("2019-01-14", NA),
ymd("2019-02-25", "2020-01-01")
)
)
#> [1] "2019-02-25T23:59:59" "2020-01-01T23:59:59"ADaM requires that date or datetime variables for which imputation
was used are accompanied by date and/or time imputation flag variables
(*DTF and *TMF, e.g., ADTF and
ATMF for ADTM). These variables indicate the
highest level that was imputed, e.g., if minutes and seconds were
imputed, the imputation flag is set to "M". The
{admiral} functions which derive imputed variables are also
adding the corresponding imputation flag variables.
Note: The {admiral} datetime imputation function
provides the ignore_seconds_flag argument which can be set
to TRUE in cases where seconds were never collected. This
is due to the following from ADaM IG: For a given SDTM DTC variable, if
only hours and minutes are ever collected, and seconds are imputed in
*DTM as 00, then it is not necessary to set
*TMF to "S".
{admiral} provides the following functions for
imputation:
derive_vars_dt(): Adds a date variable and a date
imputation flag variable (optional) based on a –DTC variable and
imputation rules.derive_vars_dtm(): Adds a datetime variable, a date
imputation flag variable, and a time imputation flag variable (both
optional) based on a –DTC variable and imputation rules.impute_dtc_dtm(): Returns a complete ISO 8601 datetime
or NA based on a partial ISO 8601 datetime and imputation
rules.impute_dtc_dt(): Returns a complete ISO 8601 date
(without time) or NA based on a partial ISO 8601 date(time)
and imputation rules.convert_dtc_to_dt(): Returns a date if the input ISO
8601 date is complete. Otherwise, NA is returned.convert_dtc_to_dtm(): Returns a datetime if the input
ISO 8601 date is complete (with missing time replaced by
"00:00:00" as default). Otherwise, NA is returned.compute_dtf(): Returns the date imputation flag.compute_tmf(): Returns the time imputation flag.The derive_vars_dtm() function derives an imputed
datetime variable and the corresponding date and time imputation flags.
The imputed date variable can be derived by using the
derive_vars_dtm_to_dt() function. It is not necessary and
advisable to perform the imputation for the date variable if it was
already done for the datetime variable. CDISC considers the datetime and
the date variable as two representations of the same date. Thus the
imputation must be the same and the imputation flags are valid for both
the datetime and the date variable.
ae <- tribble(
~AESTDTC,
"2019-08-09T12:34:56",
"2019-04-12",
"2010-09",
NA_character_
) %>%
derive_vars_dtm(
dtc = AESTDTC,
new_vars_prefix = "AST",
highest_imputation = "M",
date_imputation = "first",
time_imputation = "first"
) %>%
derive_vars_dtm_to_dt(exprs(ASTDTM))| AESTDTC | ASTDTM | ASTDTF | ASTTMF | ASTDT |
|---|---|---|---|---|
| 2019-08-09T12:34:56 | 2019-08-09 12:34:56 | NA | NA | 2019-08-09 |
| 2019-04-12 | 2019-04-12 00:00:00 | NA | H | 2019-04-12 |
| 2010-09 | 2010-09-01 00:00:00 | D | H | 2010-09-01 |
| NA | NA | NA | NA | NA |
If an imputed date variable without a corresponding datetime variable
is required, it can be derived by the derive_vars_dt()
function.
ae <- tribble(
~AESTDTC,
"2019-08-09T12:34:56",
"2019-04-12",
"2010-09",
NA_character_
) %>%
derive_vars_dt(
dtc = AESTDTC,
new_vars_prefix = "AST",
highest_imputation = "M",
date_imputation = "first"
)| AESTDTC | ASTDT | ASTDTF |
|---|---|---|
| 2019-08-09T12:34:56 | 2019-08-09 | NA |
| 2019-04-12 | 2019-04-12 | NA |
| 2010-09 | 2010-09-01 | D |
| NA | NA | NA |
If the time should be imputed but not the date, the
highest_imputation argument should be set to
"h". This results in NA if the date is
partial. As no date is imputed the date imputation flag is not
created.
ae <- tribble(
~AESTDTC,
"2019-08-09T12:34:56",
"2019-04-12",
"2010-09",
NA_character_
) %>%
derive_vars_dtm(
dtc = AESTDTC,
new_vars_prefix = "AST",
highest_imputation = "h",
time_imputation = "first"
)| AESTDTC | ASTDTM | ASTTMF |
|---|---|---|
| 2019-08-09T12:34:56 | 2019-08-09 12:34:56 | NA |
| 2019-04-12 | 2019-04-12 00:00:00 | H |
| 2010-09 | NA | NA |
| NA | NA | NA |
Usually the adverse event start date is imputed as the earliest date
of all possible dates when filling the missing parts. The result may be
a date before treatment start date. This is not desirable because the
adverse event would not be considered as treatment emergent and excluded
from the adverse event summaries. This can be avoided by specifying the
treatment start date variable (TRTSDTM) for the
min_dates argument.
Please note that TRTSDTM is used as imputed date only if
the non missing date and time parts of AESTDTC coincide
with those of TRTSDTM. Therefore 2019-10 is
not imputed as 2019-11-11 12:34:56. This ensures that
collected information is not changed by the imputation.
ae <- tribble(
~AESTDTC, ~TRTSDTM,
"2019-08-09T12:34:56", ymd_hms("2019-11-11T12:34:56"),
"2019-10", ymd_hms("2019-11-11T12:34:56"),
"2019-11", ymd_hms("2019-11-11T12:34:56"),
"2019-12-04", ymd_hms("2019-11-11T12:34:56")
) %>%
derive_vars_dtm(
dtc = AESTDTC,
new_vars_prefix = "AST",
highest_imputation = "M",
date_imputation = "first",
time_imputation = "first",
min_dates = exprs(TRTSDTM)
)| AESTDTC | TRTSDTM | ASTDTM | ASTDTF | ASTTMF |
|---|---|---|---|---|
| 2019-08-09T12:34:56 | 2019-11-11 12:34:56 | 2019-08-09 12:34:56 | NA | NA |
| 2019-10 | 2019-11-11 12:34:56 | 2019-10-01 00:00:00 | D | H |
| 2019-11 | 2019-11-11 12:34:56 | 2019-11-11 12:34:56 | D | H |
| 2019-12-04 | 2019-11-11 12:34:56 | 2019-12-04 00:00:00 | NA | H |
If a date is imputed as the latest date of all possible dates when
filling the missing parts, it should not result in dates after data cut
off or death. This can be achieved by specifying the dates for the
max_dates argument.
Please note that non missing date parts are not changed. Thus
2019-12-04 is imputed as 2019-12-04 23:59:59
although it is after the data cut off date. It may make sense to replace
it by the data cut off date but this is not part of the imputation. It
should be done in a separate data cleaning or data cut off step.
ae <- tribble(
~AEENDTC, ~DTHDT, ~DCUTDT,
"2019-08-09T12:34:56", ymd("2019-11-11"), ymd("2019-12-02"),
"2019-11", ymd("2019-11-11"), ymd("2019-12-02"),
"2019-12", NA, ymd("2019-12-02"),
"2019-12-04", NA, ymd("2019-12-02")
) %>%
derive_vars_dtm(
dtc = AEENDTC,
new_vars_prefix = "AEN",
highest_imputation = "M",
date_imputation = "last",
time_imputation = "last",
max_dates = exprs(DTHDT, DCUTDT)
)| AEENDTC | DTHDT | DCUTDT | AENDTM | AENDTF | AENTMF |
|---|---|---|---|---|---|
| 2019-08-09T12:34:56 | 2019-11-11 | 2019-12-02 | 2019-08-09 12:34:56 | NA | NA |
| 2019-11 | 2019-11-11 | 2019-12-02 | 2019-11-11 23:59:59 | D | H |
| 2019-12 | NA | 2019-12-02 | 2019-12-02 23:59:59 | D | H |
| 2019-12-04 | NA | 2019-12-02 | 2019-12-04 23:59:59 | NA | H |
If imputation is required without creating a new variable the
convert_dtc_to_dt() function can be called to obtain a
vector of imputed dates. It can be used for example in conditions:
mh <- tribble(
~MHSTDTC, ~TRTSDT,
"2019-04", ymd("2019-04-15"),
"2019-04-01", ymd("2019-04-15"),
"2019-05", ymd("2019-04-15"),
"2019-06-21", ymd("2019-04-15")
) %>%
filter(
convert_dtc_to_dt(
MHSTDTC,
highest_imputation = "M",
date_imputation = "first"
) < TRTSDT
)| MHSTDTC | TRTSDT |
|---|---|
| 2019-04 | 2019-04-15 |
| 2019-04-01 | 2019-04-15 |
Using different imputation rules depending on the observation can be
done by using slice_derivation().
vs <- tribble(
~VSDTC, ~VSTPT,
"2019-08-09T12:34:56", NA,
"2019-10-12", "PRE-DOSE",
"2019-11-10", NA,
"2019-12-04", NA
) %>%
slice_derivation(
derivation = derive_vars_dtm,
args = params(
dtc = VSDTC,
new_vars_prefix = "A"
),
derivation_slice(
filter = VSTPT == "PRE-DOSE",
args = params(time_imputation = "first")
),
derivation_slice(
filter = TRUE,
args = params(time_imputation = "last")
)
)| VSDTC | VSTPT | ADTM | ATMF |
|---|---|---|---|
| 2019-08-09T12:34:56 | NA | 2019-08-09 12:34:56 | NA |
| 2019-11-10 | NA | 2019-11-10 23:59:59 | H |
| 2019-12-04 | NA | 2019-12-04 23:59:59 | H |
| 2019-10-12 | PRE-DOSE | 2019-10-12 00:00:00 | H |
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.