The OpenSAFELY framework has been developed on shoestring funding.
While feature-complete for our own purposes, it lacks several features we'd like to write to make it useful for the entire research community, and requires manual intervention at several points to complete analysis workflow.
Our vision is to bring the framework to a point where development, review and publication workflows are highly automated, the documentation is complete, and any third party can use the framework for any analysis.
Some of the areas we would like to address include (in rough order of priority):
At the moment, we test our models against an arbitrary set of test data. This contains many interesting edge cases but doesn't exhibit the expected statistical properties of real populations. This adequate for basic model development, but (for example) multivariate regressions may fail to converge against a small dummy dataset where they would be expected to success against the real dataset.
This makes model development difficult; at the moment, our epidemiologists manually override the dummy data with faked data in cases where dummy data causes our tests to fail. This approach is error-prone and awkward.
Rather than improving the fake data, we plan instead to incorporate
expectations into the study definition. For example, a covariate
hba1c may have an expectation that its results are integers normally
distributed around the value 12.6.
The full set of outcomes, covariates and expectations will be used to generate dummy data with the expected properties. It will also be used to test the expectations against the real dataset, and report back to the analyst with differences.
More detail in #69.
At the moment, when a model has been run in the secure environment, its outputs are manually reviewed by two people who have access to that environment, to check for accidental privacy leaks; for example, low numbers are manually redacted.
When they are happy with this, the changes are committed to the git repository and pushed to github.
We would like to:
- Create tools to help reviewers, such as flagging possible low numbers, providing standard redaction templates
- Add tooling so reviewers don't need to understand git commands
- Add tooling to automatically publish updates where only the underlying data (not the model) has changed
Currently, we only support Stata. We have started work on supporting analysis using R and Python via Jupyter Notebooks, and would also like to include support for Julia
Currently, when a model needs to be run, privileged users must log into the secure environment to run then, and also log in to monitor progress.
We would like to improve this both via Github integrataion (for example, tagged releases should trigger model runs when merged to the master branch), and Slack integration (model runs can be triggered - and reported back - via Slack).
As well as epidemiological analysis, it will be possible to use the framework to publish regularly-updating dashboards of summary statistics. This will require triggers and/or schedules to run and publish code.
We have currently implemented a backend for TPP data and hope to add other backends in the near future.
We would also like to consider backends for other jurisdictions outside the UK, including an OHDSI CDM backend.
When we have several backends, we will need to support automated approaches to pooling results through federated analysis.
It should be possible, using only documentation, for any third party to:
- Develop new backends
- Deploy a backend within a secure environment
- Support analysts who request their models be run, including reviewing and returning vetted outputs