Estimated Bounds on Treatment Effects via Biomarker Principal Stratification

Ransmond O. Berchie published a dataset on 2026-04-29 illustrating a statistical method for estimating causal treatment effects in randomized trials. The method uses principal stratification with a normal copula to model the correlation between potential biomarker changes under treatment and placebo. The 5.5 KB XLS file applies this approach to sodium-glucose cotransporter-2 inhibitors (SGLT-2i) and estimated glomerular filtration rate in chronic kidney disease patients.

Use Cases

• Estimating conditional average treatment effects on clinical endpoints based on acute biomarker changes.
• Illustrating principal stratification methods for handling post-randomization variables in clinical trials.
• Assessing how knowledge of acute eGFR change modifies expected SGLT-2i effects on long-term outcomes.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• File size is 5.5 KB, indicating a concise and focused illustrative example.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

Ransmond O. Berchie via figshare.

Collection Method

Likely contains results from applying a statistical model to clinical trial data.

Time Range

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Freshness

Last updated 2026-04-29 17:47:02; freshness should be verified.

Geography

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