Package: datacutr 0.1.0
Tim Barnett
datacutr: SDTM Datacut
Supports the process of applying a cut to Standard Data Tabulation Model (SDTM), as part of the analysis of specific points in time of the data, normally as part of investigation into clinical trials. The functions support different approaches of cutting to the different domains of SDTM normally observed.
Authors:
datacutr_0.1.0.tar.gz
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datacutr.pdf |datacutr.html✨
datacutr/json (API)
NEWS
| # Install 'datacutr' in R: |
| install.packages('datacutr', repos = c('https://pharmaverse.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/pharmaverse/datacutr/issues
- datacutr_ae - Adverse Events SDTMv Dataset
- datacutr_dm - Demographics SDTMv Dataset
- datacutr_ds - Disposition SDTMv Dataset
- datacutr_fa - Findings About Events or Interventions SDTMv Dataset
- datacutr_lb - Laboratory Test Results SDTMv Dataset
- datacutr_sc - Subject Characteristics SDTMv Dataset
- datacutr_ts - Trial Summary SDTMv Dataset
Last updated 1 years agofrom:6ad84ad543. Checks:OK: 7. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 20 2024 |
| R-4.5-win | OK | Aug 20 2024 |
| R-4.5-linux | OK | Aug 20 2024 |
| R-4.4-win | OK | Aug 20 2024 |
| R-4.4-mac | OK | Aug 20 2024 |
| R-4.3-win | OK | Aug 20 2024 |
| R-4.3-mac | OK | Aug 20 2024 |
Exports:apply_cutcreate_dcutdate_cutdrop_temp_varsimpute_dcutdtcimpute_sdtmprocess_cutpt_cutspecial_dm_cut
Dependencies:admiraldevassertthatclicpp11dplyrfansigenericsgluelifecyclelubridatemagrittrpillarpkgconfigpurrrR6rlangstringistringrtibbletidyrtidyselecttimechangeutf8vctrswithr
Applying a Variable Date Cut
Rendered fromvariable_cut.Rmdusingknitr::rmarkdownon Aug 20 2024.Last update: 2023-03-22
Started: 2023-03-22
Contribution to {datacutr}
Rendered fromcontribution_model.Rmdusingknitr::rmarkdownon Aug 20 2024.Last update: 2023-03-22
Started: 2023-03-22
Example Modular Approach
Rendered fromexamplemodular.Rmdusingknitr::rmarkdownon Aug 20 2024.Last update: 2023-03-27
Started: 2023-03-22
Example Wrapped Approach
Rendered fromexamplewrapped.Rmdusingknitr::rmarkdownon Aug 20 2024.Last update: 2023-03-27
Started: 2023-03-22
Get Started
Rendered fromdatacutr.Rmdusingknitr::rmarkdownon Aug 20 2024.Last update: 2023-03-22
Started: 2023-03-22
Modular Approach
Rendered frommodular.Rmdusingknitr::rmarkdownon Aug 20 2024.Last update: 2023-03-27
Started: 2023-03-22
Wrapped Approach
Rendered fromwrapper.Rmdusingknitr::rmarkdownon Aug 20 2024.Last update: 2023-03-27
Started: 2023-03-22
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Applies the datacut based on the datacut flagging variables | apply_cut |
| Create Datacut Dataset (DCUT) | create_dcut |
| Adverse Events SDTMv Dataset | datacutr_ae |
| Demographics SDTMv Dataset | datacutr_dm |
| Disposition SDTMv Dataset | datacutr_ds |
| Findings About Events or Interventions SDTMv Dataset | datacutr_fa |
| Laboratory Test Results SDTMv Dataset | datacutr_lb |
| Subject Characteristics SDTMv Dataset | datacutr_sc |
| Trial Summary SDTMv Dataset | datacutr_ts |
| xxSTDTC or xxDTC Cut | date_cut |
| Drops Temporary Variables From a Dataset | drop_temp_vars |
| Imputes Partial Date/Time Data Cutoff Variable (DCUTDTC) | impute_dcutdtc |
| Imputes Partial Date/Time SDTMv Variables | impute_sdtm |
| Wrapper function to prepare and apply the datacut of SDTMv datasets | process_cut |
| Patient Cut | pt_cut |
| Special DM Cut to reset Death variable information past cut date | special_dm_cut |
