This tool supports the authoring of OpenSAFELY-compliant research, by:
- Allowing developers to generate random data based on their study expectations. They can then use this as input data when developing analytic models.
- Supporting downloading of codelist CSVs from the OpenSAFELY codelists repository, for incorporation into the study definition
- Providing tools to understand and visualise the properties of real data, without having direct access to it
It is also the mechanism by which cohorts are extracted from live database backends within the OpenSAFELY framework.
To install the latest released version:
pip install --upgrade opensafely-cohort-extractor
To discover its options:
cohortextractor --help
It is designed to be run within an OpenSAFELY-compliant research repository. You can find a template repository to get you started here.
The tool has a remote subcommand which triggers jobs to be scheduled
to run elsewhere, via a job
server. If you set the
environment variable OPENSAFELY_REMOTE_WEBHOOK, this hook will be
supplied to the job server, which will POST a message to that URL
when a job has finished.
You can run everything in docker with ./run.sh pytest.
You can also run the tests in your own virtualenv, but either way you will (probably) still want to use docker to run a SQL Server instance:
- Start an mssql server with
docker-compose up - Set up a virtualenv and
pip install -r requirements.txt py.test tests/
Note: if you change the database schema
be sure to docker-compose stop && docker-compose rm before re-running
tests to ensure they are recreated.
To make a release, when you merge to the main branch, at least one of
your commits must contain a conventional commit prefixed fix:,
perf: or feat: (patch, patch, and minor releases, respectively);
or a final line starting BREAKING CHANGE: (major release).
Other types are ignored, but you might as well use them: docs,
style, refactor, ci, revert are likely to be the most common,
but there's a full list
here
The OpenSAFELY framework is a new secure analytics platform for electronic health records research in the NHS.
Instead of requesting access for slices of patient data and transporting them elsewhere for analysis, the framework supports developing analytics against dummy data, and then running against the real data within the same infrastructure that the data is stored. Read more at OpenSAFELY.org.
The framework is under fast, active development to support rapid analytics relating to COVID19; we're currently seeking funding to make it easier for outside collaborators to work with our system.