diff --git a/main/404.html b/main/404.html index 56d46f80..717a8200 100644 --- a/main/404.html +++ b/main/404.html @@ -58,20 +58,24 @@
  • Changelog
    • -
  • -
    main -devel -pre-release
    • -
      +
    • + + + + +
      • +
    • + + + + +
    • @@ -149,21 +153,25 @@

      Read in Data
       library(admiral)
+library(admiralvaccine)
+library(pharmaversesdtm)
 library(dplyr, warn.conflicts = FALSE)
-library(admiral.test)
 library(lubridate)
 library(stringr)
-data("vx_dm")
-data("vx_ex")
-dm <- convert_blanks_to_na(vx_dm)
-ex <- convert_blanks_to_na(vx_ex)
      +library(admiraldev) + +data("dm_vaccine") +data("ex_vaccine") + +dm <- convert_blanks_to_na(dm_vaccine) +ex <- convert_blanks_to_na(ex_vaccine)

      The DM domain is used as the basis for ADSL:

      -adsl <- dm %>%
+adsl <- dm %>%
   select(-DOMAIN)
      -
      - +
      +

      Derive Period, Subperiod, and Phase Variables @@ -171,7 +179,7 @@

      Derive Period, Subperiod, and Phase Variables

      The admiral core package has separate functions to handle period variables since these variables are study specific.

      -

      See the “Visit +

      See the “Visit and Period Variables” vignette for more information.

      If the variables are not derived based on a period reference dataset, they may be derived at a later point of the flow. For example, phases @@ -185,67 +193,67 @@

      Derive Treatment Variables (TRT0xP,

      The mapping of the treatment variables is left to the ADaM programmer. An example mapping for a study without periods may be:

      -adsl <- dm %>%
+adsl <- dm %>%
   mutate(
     TRT01P = substring(ARM, 1, 9),
     TRT02P = substring(ARM, 11, 100)
-  ) %>%
-  derive_vars_merged(
+  ) %>%
+  derive_vars_merged(
     dataset_add = ex,
     filter_add = EXLNKGRP == "VACCINATION 1",
-    new_vars = exprs(TRT01A = EXTRT),
-    by_vars = exprs(STUDYID, USUBJID)
-  ) %>%
-  derive_vars_merged(
+    new_vars = exprs(TRT01A = EXTRT),
+    by_vars = exprs(STUDYID, USUBJID)
+  ) %>%
+  derive_vars_merged(
     dataset_add = ex,
     filter_add = EXLNKGRP == "VACCINATION 2",
-    new_vars = exprs(TRT02A = EXTRT),
-    by_vars = exprs(STUDYID, USUBJID)
+    new_vars = exprs(TRT02A = EXTRT),
+    by_vars = exprs(STUDYID, USUBJID)
   )

      Derive/Impute Numeric Treatment Date/Time and Duration (TRTSDTM, TRTEDTM, TRTDURD)

      -

      The function derive_vars_merged() can be used to derive +

      The function derive_vars_merged() can be used to derive the treatment start and end date/times using the ex domain. A pre-processing step for ex is required to convert the variable EXSTDTC and EXSTDTC to datetime variables and impute missing date or time components. Conversion and -imputation is done by derive_vars_dtm().

      +imputation is done by derive_vars_dtm().

      Example calls:

       # impute start and end time of exposure to first and last respectively, do not impute date
-ex_ext <- ex %>%
-  derive_vars_dtm(
+ex_ext <- ex %>%
+  derive_vars_dtm(
     dtc = EXSTDTC,
     new_vars_prefix = "EXST"
-  ) %>%
-  derive_vars_dtm(
+  ) %>%
+  derive_vars_dtm(
     dtc = EXENDTC,
     new_vars_prefix = "EXEN"
   )
-adsl <- adsl %>%
-  derive_vars_merged(
+adsl <- adsl %>%
+  derive_vars_merged(
     dataset_add = ex_ext,
     filter_add = (EXDOSE > 0 |
       (EXDOSE == 0 &
         str_detect(EXTRT, "VACCINE"))) &
       !is.na(EXSTDTM),
-    new_vars = exprs(TRTSDTM = EXSTDTM),
-    order = exprs(EXSTDTM, EXSEQ),
+    new_vars = exprs(TRTSDTM = EXSTDTM),
+    order = exprs(EXSTDTM, EXSEQ),
     mode = "first",
-    by_vars = exprs(STUDYID, USUBJID)
-  ) %>%
-  derive_vars_merged(
+    by_vars = exprs(STUDYID, USUBJID)
+  ) %>%
+  derive_vars_merged(
     dataset_add = ex_ext,
     filter_add = (EXDOSE > 0 |
       (EXDOSE == 0 &
         str_detect(EXTRT, "VACCINE"))) & !is.na(EXENDTM),
-    new_vars = exprs(TRTEDTM = EXENDTM),
-    order = exprs(EXENDTM, EXSEQ),
+    new_vars = exprs(TRTEDTM = EXENDTM),
+    order = exprs(EXENDTM, EXSEQ),
     mode = "last",
-    by_vars = exprs(STUDYID, USUBJID)
+    by_vars = exprs(STUDYID, USUBJID)
   )

      This call returns the original data frame with the column TRTSDTM, TRTSTMF, TRTEDTM, and @@ -253,37 +261,37 @@

      Derive/Impute Numeric Treatment Date/Time and Duration and zero doses of non placebo treatments are ignored. Missing time parts are imputed as first or last for start and end date respectively.

      The datetime variables returned can be converted to dates using the -derive_vars_dtm_to_dt() function.

      +derive_vars_dtm_to_dt() function.

      -adsl <- adsl %>%
-  derive_vars_dtm_to_dt(source_vars = exprs(TRTSDTM, TRTEDTM))
      +adsl <- adsl %>% + derive_vars_dtm_to_dt(source_vars = exprs(TRTSDTM, TRTEDTM))

      Now, that TRTSDT and TRTEDT are derived, -the function derive_var_trtdurd() can be used to calculate +the function derive_var_trtdurd() can be used to calculate the Treatment duration (TRTDURD).

      -adsl <- adsl %>%
-  derive_var_trtdurd()
      -
      -
      +adsl <- adsl %>% + derive_var_trtdurd()
      +
      +

      Population Flags (e.g. SAFFL)

      Since the populations flags are mainly company/study specific no dedicated functions are provided, but in most cases they can easily be -derived using derive_var_merged_exist_flag().

      +derived using derive_var_merged_exist_flag().

      An example of an implementation could be:

      -adsl <- derive_var_merged_exist_flag(
+adsl <- derive_var_merged_exist_flag(
   dataset = adsl,
   dataset_add = ex,
-  by_vars = exprs(STUDYID, USUBJID),
+  by_vars = exprs(STUDYID, USUBJID),
   new_var = SAFFL,
   condition = (EXDOSE > 0 | (EXDOSE == 0 & str_detect(EXTRT, "VACCINE")))
-) %>%
+) %>%
   mutate(
     PPROTFL = "Y"
   )
      -
      - +
      +
      @@ -301,11 +309,11 @@

      Derive Vaccination Date Variablesadsl <- derive_vars_vaxdt( dataset = ex, dataset_adsl = adsl, - by_vars = exprs(USUBJID, VISITNUM), - order = exprs(USUBJID, VISITNUM, VISIT, EXSTDTC) + by_vars = exprs(USUBJID, VISITNUM), + order = exprs(USUBJID, VISITNUM, VISIT, EXSTDTC) )

      -
      -

      This call would return the input dataset with columns +

      +

      This call would return the input dataset with columns VAX01DT, VAX02DT added.

      @@ -314,15 +322,15 @@

      Create Period Variables (Study Specific)In this step this we will create period variables which will be study specific, User can change the logic as per their study requirement.

      -adsl <- adsl %>%
+adsl <- adsl %>%
   mutate(
     AP01SDT = VAX01DT,
     AP01EDT = if_else(!is.na(VAX02DT), VAX02DT - 1, as.Date(RFPENDTC)),
     AP02SDT = if_else(!is.na(VAX02DT), VAX02DT, NA_Date_),
     AP02EDT = if_else(!is.na(AP02SDT), as.Date(RFPENDTC), NA_Date_)
   )
      -
      -

      This call would return the input dataset with columns +

      +

      This call would return the input dataset with columns AP01SDT, AP01EDT, AP02SDT, AP02EDT added.

      @@ -334,16 +342,16 @@

      Derive Other VariablesThe following functions are helpful for many ADSL derivations:

       -

      See also Generic +

      See also Generic Functions.

      @@ -397,7 +405,7 @@

      Example Script

      -

      Developed by Sukalpo Saha, Vikram S, Ben Straub, Arjun Rubalingam, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson.

      +

      Developed by Sukalpo Saha, Arjun Rubalingam, Ben Straub, Vikram S, Dhivya Kanagaraj, Federico Baratin, Yamini Purna Bollu, Ilse Augustyns, Kalyani Bodicherla, Hilde Delanghe, Pfizer Inc., GlaxoSmithKline LLC, Johnson & Johnson.

      diff --git a/main/articles/index.html b/main/articles/index.html index f076b3e5..b5c66199 100644 --- a/main/articles/index.html +++ b/main/articles/index.html @@ -36,18 +36,22 @@
    • Changelog
      • -
    • -
      main -devel -pre-release
      • -