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vignettes/contribution_model.Rmd
+ contribution_model.Rmd
This vignette gives you an overview of the anticipated contribution +model. The goal is to enable users of datacutr to +contribute and test the contribution model for datacutr. +Adjustments might be made over time.
+For each new contribution, the user creates an issue on the issue +tab on GitHub to put +it in our backlog.
All created issues +will be reviewed and the creator will receive an initial feedback via a +comment. Someone from the core development team will then triage new +issues by assigning the appropriate labels (such as “user request” so we +can easily identify new requests).
The main idea of datacutr is to provide a standardized
+approach to applying a datacut to SDTM datasets.
+The process applied by the package is the following,
DCUT
that references all patients
+to be included within the cut, and the cut date to be used as reference
+(normally the Clinical Cut-off Date that data has been cleaned
+to).DCUT
as reference, records can be removed from
+the SDTM data that are either a) patients not part of the reference
+DCUT
, or b) records that can be identified as after the
+data cut date supplied.The package relies on creating lists of SDTM to be processed in +specific ways, these include,
+DCUT
are kept, no other exclusion of records is
+conductedDCUT
+are kept, and records identified after the data cut date are
+removedDM
contains critical temporal
+derivations around Deaths that would require update within a data cut,
+this option allows the user to revert DM.DTHFL
and
+DM.DTHDTC
if death is identified after the data cut
+dateThe datacutr package allows two different approaches +for the user to apply the data cut process
+All functions are reviewed and tested to ensure that they work as +described in the documentation. Testing has been done on SDTMIG v3.2 +(SDTM v1.4).
+datacutr provides a template R scripts as a starting +point. See Modular +Approach and Wrapped +Approach for more details.
+This article is an example workflow of the modular approach where +each section of the cut is explicitly called.
+
+# Name: Datacut Template Code - Modular Approach
+
+# Creating data to be cut ------------------------------------------------
+library(datacutr)
+library(admiraldev)
+library(dplyr)
+library(lubridate)
+library(stringr)
+library(purrr)
+
+source_data <- list(
+ ds = datacutr_ds, dm = datacutr_dm, ae = datacutr_ae, sc = datacutr_sc,
+ lb = datacutr_lb, fa = datacutr_fa, ts = datacutr_ts
+)
+
+
+# Create DCUT ------------------------------------------------------------
+
+dcut <- create_dcut(
+ dataset_ds = source_data$ds,
+ ds_date_var = DSSTDTC,
+ filter = DSDECOD == "RANDOMIZATION",
+ cut_date = "2022-06-04",
+ cut_description = "Clinical Cutoff Date"
+)
+
+
+# Pre-processing of FA ----------------------------------------------------
+
+# Update FA
+source_data$fa <- source_data$fa %>%
+ mutate(DCUT_TEMP_FAXDTC = case_when(
+ FASTDTC != "" ~ FASTDTC,
+ FADTC != "" ~ FADTC,
+ TRUE ~ as.character(NA)
+ ))
+
+
+# Specify cut types ------------------------------------------------------
+
+# Patient cut - cut applied will only be for patients existing in DCUT
+patient_cut_list <- c("sc", "ds")
+
+# Date cut - cut applied will be both for patients existing in DCUT, and date cut against DCUTDTM
+date_cut_list <- rbind(
+ c("ae", "AESTDTC"),
+ c("lb", "LBDTC"),
+ c("fa", "DCUT_TEMP_FAXDTC")
+)
+
+# No cut - data does not need to be cut
+no_cut_list <- list(ts = source_data$ts)
+
+
+# Create the cutting variables -------------------------------------------
+
+# Conduct the patient cut ------------------------------------------------
+patient_cut_data <- lapply(
+ source_data[patient_cut_list], pt_cut,
+ dataset_cut = dcut
+)
+
+# Conduct xxSTDTC or xxDTC Cut -------------------------------------------
+date_cut_data <- pmap(
+ .l = list(
+ dataset_sdtm = source_data[date_cut_list[, 1]],
+ sdtm_date_var = syms(date_cut_list[, 2])
+ ),
+ .f = date_cut,
+ dataset_cut = dcut,
+ cut_var = DCUTDTM
+)
+
+# Conduct DM special cut for DTH flags after DCUTDTM ---------------------
+dm_cut <- special_dm_cut(
+ dataset_dm = source_data$dm,
+ dataset_cut = dcut,
+ cut_var = DCUTDTM
+)
+
+
+# Apply the cut --------------------------------
+
+cut_data <- purrr::map(
+ c(patient_cut_data, date_cut_data, list(dm = dm_cut)),
+ apply_cut,
+ dcutvar = DCUT_TEMP_REMOVE,
+ dthchangevar = DCUT_TEMP_DTHCHANGE
+)
+
+# Add on data which is not cut
+final_data <- c(cut_data, no_cut_list, list(dcut = dcut))
This article is an example workflow of the wrapped approach where +modules are wrapped into a cut_data function.
+
+# Name: Datacut Template Code - Modular Approach
+
+# Creating data to be cut ------------------------------------------------
+library(datacutr)
+library(admiraldev)
+library(dplyr)
+library(lubridate)
+library(stringr)
+library(purrr)
+
+# Name: Datacut Template Code - Wrapped Approach
+
+# Creating data to be cut ------------------------------------------------
+
+source_data <- list(
+ ds = datacutr_ds, dm = datacutr_dm, ae = datacutr_ae, sc = datacutr_sc,
+ lb = datacutr_lb, fa = datacutr_fa, ts = datacutr_ts
+)
+
+# Create DCUT ------------------------------------------------------------
+
+dcut <- create_dcut(
+ dataset_ds = source_data$ds,
+ ds_date_var = DSSTDTC,
+ filter = DSDECOD == "RANDOMIZATION",
+ cut_date = "2022-06-04",
+ cut_description = "Clinical Cutoff Date"
+)
+
+
+# Pre-processing of FA ----------------------------------------------------
+
+# Update FA
+source_data$fa <- source_data$fa %>%
+ mutate(DCUT_TEMP_FAXDTC = case_when(
+ FASTDTC != "" ~ FASTDTC,
+ FADTC != "" ~ FADTC,
+ TRUE ~ as.character(NA)
+ ))
+
+
+# Process data cut --------------------------------------------------------
+
+cut_data <- process_cut(
+ source_sdtm_data = source_data,
+ patient_cut_v = c("sc", "ds"),
+ date_cut_m = rbind(
+ c("ae", "AESTDTC"),
+ c("lb", "LBDTC"),
+ c("fa", "DCUT_TEMP_FAXDTC")
+ ),
+ no_cut_v = c("ts"),
+ dataset_cut = dcut,
+ cut_var = DCUTDTM,
+ special_dm = TRUE
+)
This article describes how to cut study SDTM data using a modular +approach to enable any further study or project specific +customization.
+The next step is to create the DCUT dataset containing the datacut +date and description.
+
+dcut <- create_dcut(
+ dataset_ds = source_data$ds,
+ ds_date_var = DSSTDTC,
+ filter = DSDECOD == "RANDOMIZATION",
+ cut_date = "2022-06-04",
+ cut_description = "Clinical Cutoff Date"
+)
If any pre-processing of datasets is needed, for example in the case +of FA, where there are multiple date variables, this should be done +next.
+
+source_data$fa <- source_data$fa %>%
+ mutate(DCUT_TEMP_FAXDTC = case_when(
+ FASTDTC != "" ~ FASTDTC,
+ FADTC != "" ~ FADTC,
+ TRUE ~ as.character(NA)
+ ))
We’ll next specify the cut types for each dataset (patient cut, date +cut or no cut) and in the case of date cut which date variable should be +used.
+ +Next we’ll apply the patient cut.
+
+patient_cut_data <- lapply(
+ source_data[patient_cut_list], pt_cut,
+ dataset_cut = dcut
+)
This adds on temporary flag variables indicating which observations +will be removed, for example for SC:
+ + +Next we’ll apply the date cut.
+
+date_cut_data <- pmap(
+ .l = list(
+ dataset_sdtm = source_data[date_cut_list[, 1]],
+ sdtm_date_var = syms(date_cut_list[, 2])
+ ),
+ .f = date_cut,
+ dataset_cut = dcut,
+ cut_var = DCUTDTM
+)
This again adds on temporary flag variables indicating which +observations will be removed, for example for AE:
+ + +Then lastly we’ll apply the special DM cut which also updates the +death related variables.
+
+dm_cut <- special_dm_cut(
+ dataset_dm = source_data$dm,
+ dataset_cut = dcut,
+ cut_var = DCUTDTM
+)
This adds on temporary variables indicating any death records that +would change as a result of applying a datacut:
+ + +This article describes how to apply a data cut, when the date to
+apply is not the more common singular date, but a different date per
+patient. An example would be to cut all patients data at their week 24
+visit date. The below is an example how this can be done utilizing
+datacutr
.
The next step is to create the DCUT
dataset containing
+the description, and a fixed date that ensures all data necessary from
+ds
is included into DCUT
. An example would be
+today’s date.
+dcut <- create_dcut(
+ dataset_ds = source_data$ds,
+ ds_date_var = DSSTDTC,
+ filter = DSDECOD == "RANDOMIZATION",
+ cut_date = as.character(lubridate::today()),
+ cut_description = "Week 24 Cut"
+)
The next step is to update DCUT
with the required date
+per patient required for the variable cut. An example is below using the
+trial visits as source. If the required event has not been observed,
+keeping DCUT.DCUTDTC
as the future/today date ensures all
+data is kept within the cut for that patient.
+sv <- tibble::tribble(
+ ~USUBJID, ~VISIT, ~SVSTDTC,
+ "AB12345-001", "WEEK24", "2022-06-01",
+ "AB12345-002", "WEEK24", "2022-06-30",
+ "AB12345-003", "WEEK24", "2022-07-01",
+ "AB12345-004", "WEEK24", "2022-05-04",
+)
+
+dcut <- dcut %>%
+ left_join(sv %>%
+ filter(VISIT == "WEEK24") %>%
+ select(USUBJID, SVSTDTC)) %>%
+ mutate(DCUTDTC = as.character(ifelse(!is.na(SVSTDTC), SVSTDTC, as.character(DCUTDTC)))) %>%
+ impute_dcutdtc(dsin = ., varin = DCUTDTC, varout = DCUTDTM)
Now that DCUT
is prepared, the rest of the process
+follows the same as previously prescribed using either the wrapped
+function approach Link
+or modular approach Link