OnsetBounds

OnsetBounds is the research software accompanying Sharp performance bounds for clinical early-warning systems with interval-uncertain event onset: a partial-identification framework by Zhuowei Gao and Weipeng Sun, School of Mathematics, Jilin University.

Overview

Clinical event onset may be observed only within an interval ([L,U]), rather than at an exact time. OnsetBounds computes sharp single-policy performance bounds and direct paired comparison bounds while preserving this interval uncertainty.

The framework separates identification uncertainty due to interval-uncertain onset from sampling uncertainty in the estimated bounds.

Installation

git clone https://github.com/gaozw23/OnsetBounds.git
cd OnsetBounds
python -m venv .venv
python -m pip install --upgrade pip
python -m pip install -e ".[simulation,dev]"

Reproducing the paper

Synthetic example

python paper_repro/synthetic/generate_synthetic_example.py --config paper_repro/synthetic/example_config.yaml --output paper_repro/synthetic/example_output

The command generates synthetic records only and does not use clinical data.

Simulation

The public simulation runner and example configuration are under scripts/simulation/. The full manuscript design and aggregate outputs are described in the reproducibility guide.

Figures and tables

python paper_repro/build_figures.py --input paper_repro/aggregate/figure_data --output paper_repro/reproduced_figures

The figure builder uses the public aggregate sources under paper_repro/aggregate/.

MIMIC-IV and eICU

Authorized-data interfaces and configuration templates are documented with the MIMIC-IV and eICU code. Reproduction of the clinical analyses requires local database access.

Data access

Raw MIMIC-IV and eICU data and patient-level derived records are not distributed. See docs/DATA_ACCESS.md.

Code version

The reported numerical results correspond to tag v1.0.0-submission and commit 6df6e4b7679a4dcfcd9ddf17b9e5a7f9dab2d097. Later documentation changes do not alter that statistical implementation. See docs/PROVENANCE.md for the bootstrap provenance.

Tests

The public test suite contains 17 pytest functions covering the implementation checks summarized in Supplementary Table S12. The correspondence is listed in docs/TEST_CASE_MAPPING.csv. Cohort containment is verified in the completed simulation outputs rather than by a standalone unit test.

python -m pytest -q

Citation

Please cite Sharp performance bounds for clinical early-warning systems with interval-uncertain event onset: a partial-identification framework by Zhuowei Gao and Weipeng Sun, School of Mathematics, Jilin University. Machine-readable citation metadata are provided in CITATION.cff.

License

This project is released under the MIT License. See LICENSE.

External Python packages retain their respective upstream licenses; no third-party source code is vendored in this repository.