Skip to contents


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.

Programming Flow

Read in Data

To start, all SDTM data to be cut needs to be read into the environment. These should be stored in a list.


source_data <- list(ds=ds, dm=dm, ae=ae, sc=sc, lb=lb, fa=fa, ts=ts)

Create DCUT Dataset

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 = ds,
                    ds_date_var = DSSTDTC,
                    filter = DSDECOD == "RANDOMIZATION",
                    cut_date = lubridate::today(),
                    cut_description = "Week 24 Cut")

Postprocess DCUT

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(
    "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.Date(ifelse(!,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