https://github.com/dominodatalab/clinical_study_template
Repo containing the directory structure and programs to create a reporting effort (RE) project.
https://github.com/dominodatalab/clinical_study_template
clinical-programming demo-asset pharma sce
Last synced: 7 months ago
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Repo containing the directory structure and programs to create a reporting effort (RE) project.
- Host: GitHub
- URL: https://github.com/dominodatalab/clinical_study_template
- Owner: dominodatalab
- Created: 2023-05-04T17:24:47.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-18T22:50:56.000Z (11 months ago)
- Last Synced: 2025-01-24T18:27:07.348Z (8 months ago)
- Topics: clinical-programming, demo-asset, pharma, sce
- Language: SAS
- Homepage:
- Size: 134 KB
- Stars: 0
- Watchers: 7
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
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README
# Template Clinical Study Reporting repository
This project template contains the folder structure and templated code (ADaM & TFL) to populate clinical study project.
This repo project is copied by the Clinical Programming team to instantiate a new study specfic reporting effort GitHub repo and Domino Project.
Each reporting effort for a study will be a new branch from this single overall clinical study repository.
# Directory structure
The programming is created in a typical clinical trial folder structure, where the production (prod) and qc programs have independent directory trees.
Reporting effort level standard code (e.g. SAS macros) should be stored in the `share/macros` folder.
The global `domino.sas` autoexec progam is also included in the repository to appropriately set up the SAS environment.
```
repo
│ domino.sas
├───prod
│ ├───adam
├───adam_flows
├───tfl
│ └───tfl_flows
├───qc
│ ├───adam
│ │ compare_adam.sas
├───adam_flows
├───tfl
│ └───tfl_flows
├───utilities
│ init_datasets_re.py
│ import_metadata.sas
├───flows
│ flow_1.py
│ flow_2.py
│ flow_3.py
│ flow_4.py
│ example_flow.py
└───share
└───macros
```
# Setup
1. Create a new project, named `CDISC01_RE_YOURNAME`, from the project template. This will create a new project and a new study specfic GitHub repo.
1. Run `utilities/dataset_init_re.py` as a job to create the appropriate analysis domino datasets (ADAM, TFL, ADAMQC, TFLQC and METADATA). As well as import SDTM datasets from an existing project following the same naming convention (`CDISC01_SDTM` for `CDISC01_RE_XXXXX`).
1. Add the external data volume (EDV) `metadata-repository` to your project.
a. Ask your Domino contact on how to set up this example EDV within your Domino deployment.
1. Set the `DCUTDTC` project environment variable to the same value as the latest SDTM Snapshot e.g. MAR122024.
2. Set the `SDTM_DATASET` project environment variable to `SDTMBLIND`.
1. Run `utilities/import_metadata.sas` as a job (on the SAS environment!) to move and transform the metadata Excel file stored in the `metadata-repository` EDV to sas7bdat files in your local METADATA project dataset.
1. Run each of your prod ADaM and TFL programs as seperate jobs to produce your outputs.
2. To run all the ADaM and TFL programs using the `multijob.py` utility, first you need to add your SAS enviroment ID `pipeplines/jobs_example.cfg`.
3. Once added, input the following into your job command to run the full, dependancy aware study pipeline
`pipelines/multijob.py pipelines/jobs_example.cfg`
a. Ensure you select a Python enviroment to run this pipeline head jobs within.
# Naming convention
The programs follow a typical clinical trial naming convention, where the ADaM programs are named using the dataset name (e.g. ADSL.sas, etc.) and the TFL programs have a `t_` prefix to indicate tables, etc.
# QC programming and reporting
The QC programming is all in SAS, and there is a `compare.sas` program which uses SAS PROC COMPARE to create a summary report of all differences between the prod and qc datasets. This program also generates the `dominostats.json` files which Domino uses to display a dashboard in the jobs screen.
`compare.sas` references a read-only SAS macro stored in the [`SCE_STANDARD_LIB`](https://github.com/dominodatalab/SCE_STANDARD_LIB) repo so ensure this is imported as a secondary repo in order to run it.
# Support
Programming was created by Veramed Ltd. on behalf of Domino Data Lab, Inc.