Template Reporting Effort repository

This repo and corresponding Domino Project contains the folder structure to template a reporting effort making ADaM and TFL code for the Domino clinical trial demo.

This repo project is copied by Clinical Programming team to instantiate a new study specfic reporting effort GitHub repo and Domino Project.

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
│   └───tfl
├───qc
│   ├───adam
│   │       compare_adam.sas
│   └───tfl
├───utilities
│       init_datasets.py
│       import_metadata.sas
├───pipelines
│       snakemake.sh
│       Snakefile
│       multijob.py
│       jobs_example.cfg
└───share
    └───macros

Setup

1. Create a new project, named CDISC01_RE_YOURNAME, from copying this project. This will create a new project and a new study specfic GitHub repo.

2. Run utilities/dataset_init.py as a job to create the appropriate analysis domino datasets (ADAM, TFL, ADAMQC and TFLQC). As well as import SDTM datasets from an existing project following the same naming convention (CDISC01_SDTM for CDISC01_RE_XXXXX).

3. 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.

4. Import the CDISC01_SDTM project within artifacts to get the DCUTDTC environment variable.

5. Add SCE_STANDARD_LIB as a secondary imported Git repo to your project.

6. 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.

7. Run each of your prod ADaM and TFL programs in the Jobs view to produce your outputs.

8. Within the project start the app to see the visual dependency graph.

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 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.