In epijim/admiral: ADaM in R Asset Library

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Introduction

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.

Required Packages

The examples of this vignette require the following packages.

library(admiral)
library(lubridate)
library(tibble)
library(dplyr)

Imputation Rules

The simplest imputation rule is to set the missing parts to a fixed value. For example

impute_dtc(
  "2019-10",
  date_imputation = "01-01",
  time_imputation = "00:00:00"
)

Sometimes this does not work as it would result in invalid dates, e.g.,

impute_dtc(
  "2019-02",
  date_imputation = "02-31",
  time_imputation = "00:00:00"
)

Therefore the keywords "first" or "last" can be specified to request that missing parts are replaced by the first or last possible value:

impute_dtc(
  "2019-02",
  date_imputation = "last",
  time_imputation = "00:00:00"
)

Minimum/Maximum Dates

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 parameter to specify those dates. For example:

impute_dtc(
  "2019-02",
  date_imputation = "last",
  time_imputation = "last",
  max_dates = list(ymd("2019-01-14"), ymd("2019-02-25"))
)

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.

Imputation Flags

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.

Imputation Functions

{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(): Returns a complete ISO 8601 date or NA based on a partial ISO 8601 date 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 and time is complete. Otherwise, NA is returned.
• compute_dtf(): Returns the date imputation flag.
• compute_tmf(): Returns the time imputation flag.

Examples

Create an Imputed Datetime and Date Variable and Imputation Flag Variables

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 a simple mutate() call. 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 representation 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",
    date_imputation = "first",
    time_imputation = "first"
  ) %>%
  mutate(ASTDT = date(ASTDTM))

dataset_vignette(ae)

Create an Imputed Date Variable and Imputation Flag Variable

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",
                 date_imputation = "first")

dataset_vignette(ae)

Impute Time without Imputing Date

If the time should be imputed but not the date, the date_imputation parameter should be set to NULL. 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",
    date_imputation = NULL,
    time_imputation = "first"
  )

dataset_vignette(ae)

Avoid Imputed Dates Before a Particular Date

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 parameter.

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",
    date_imputation = "first",
    time_imputation = "first",
    min_dates = vars(TRTSDTM)
  )

dataset_vignette(ae)

Avoid Imputed Dates After a Particular Date

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 parameter.

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",
    date_imputation = "last",
    time_imputation = "last",
    max_dates = vars(DTHDT, DCUTDT)
  )

dataset_vignette(ae)

Imputation Without Creating a New Variable

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, date_imputation = "first") < TRTSDT
)

dataset_vignette(mh)

Using More Than One Imputation Rule for a Variable

Using different imputation rules depending on the observation can be done by creating the variables with mutate(). First the imputed datetime variable is created calling if_else() and convert_dtc_to_dtm(). Then the time imputation flag is created calling compute_tmf(). Here no date imputation flag is created because no date imputation is performed (date_imputation = NULL).

vs <- tribble(
  ~VSDTC,                ~VSTPT,
  "2019-08-09T12:34:56", NA,
  "2019-10-12",          "PRE-DOSE",
  "2019-11-10",          NA,
  "2019-12-04",          NA
) %>% 
mutate(
  ADTM = if_else(
    VSTPT == "PRE-DOSE",
    convert_dtc_to_dtm(
      dtc = VSDTC,
      date_imputation = NULL,
      time_imputation = "first"
    ),
    convert_dtc_to_dtm(
      dtc = VSDTC,
      date_imputation = NULL,
      time_imputation = "last"
    )
  ),
  ADTMF = compute_tmf(
    dtc = VSDTC,
    dtm = ADTM
  )
)

dataset_vignette(vs)

epijim/admiral documentation built on Feb. 13, 2022, 12:15 a.m.