dansk-register

A platform for epidemiological studies on the Danish national registers, and the study pipelines built on it.

The package contains no data. It is the code that reads a register delivery inside a secure research environment, builds cohorts and outcomes from it, and estimates and reports the results.

What is in it

The dependency direction is one way and enforced by a test: the platform never imports a study, and studies never import each other. Code that turns out to be general is lifted into dansk_register rather than shared sideways.

Install

pip install dansk-register-platform                 # everything, all three studies
pip install "dansk-register-platform[study2]"       # names a study; installs the same files
pip install "dansk-register-platform[all]"          # all three, explicitly

The distribution was renamed from dansk-register at 0.7.0, to get past the analysis server mirror's 24-hour cache. The import packages are deliberately unchanged, so nothing in this codebase or on the server imports anything different; only pip install names the new one. The old name still resolves, to a distribution that stopped being updated before the rename.

The figures extra is optional on purpose. The estimators are written in polars and validated against R, so the pipeline itself carries no numeric stack; matplotlib brings numpy and is needed only to draw. Without it every result is still produced and the figure stage records that it could not render.

Commands

edu-labour --list-profiles                          # what each profile runs
edu-labour-preflight --raw-root PATH                # what a delivery contains
edu-labour --output-dir RUN --profile production \
    --raw-root PATH --allow-expensive-stages --resume
edu-labour-figures --bundle RUN/outputs/export --out figures

societal-costs --output-dir RUN --profile production

Add --dry-run to any edu-labour invocation to resolve the plan and settings without touching data.

Design notes

Stages talk through files. Every stage writes named artifacts into a run directory and reads its inputs from there, so any stage can be re-run alone against a previous run's outputs, and --resume can skip what is already done.

Estimators are written against polars. Aalen-Johansen, Fine-Gray, Cox and the multi-state occupancy are implemented directly rather than taken from a modelling library, and each is validated against reference values from R with those values checked into the tests. That keeps the numbers checkable line by line and avoids a dependency that may not install in an offline environment.

Disclosure control is part of the pipeline. Results leave the secure environment as a bundle of CSV tables with small cells suppressed and the estimates resting on them blanked. Figures are rendered from that bundle rather than from the raw artifacts, so a figure cannot show what the table beside it was not cleared to show.

What this covers, and what it does not

Secure-environment research is usually described with the Five Safes, and it is worth saying plainly which of the five a piece of software can touch at all.

"Partly" is the operative word in both rows. The output checks are cell-by-cell and cannot see a small number recovered by differencing across tables; each bundle's MANIFEST.txt says so in its own text. Running this framework is not a substitute for your host's output review, and it does not make an unapproved analysis approved.

Status

Research code, developed alongside the studies it implements. The public API is not stable.

Documentation

docs/README.md is the index, and it says which document is a standing contract and which is a finished pass kept because the code cites it. Two worth naming: docs/open_questions.md is the open-items register, and docs/REUSABILITY_ANALYSIS.md is why the platform is shaped the way it is.

Knowledge rather than implementation — register semantics, methodology, study protocols — lives in the PhD store, not here. docs/README.md states the rule that decides which is which.

Licence

MIT. See LICENSE.