pyrbmi

Python implementation of reference-based multiple imputation for regulatory clinical trials.

Direct feature-parity target: R's rbmi (openpharma) and the ICH E9(R1) estimands framework.

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Installation

# Recommended: uv
uv add pyrbmi

# pip
pip install pyrbmi

# With Bayesian backend (PyMC)
uv add "pyrbmi[bayes]"

Requires: Python ≥ 3.11

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Quick Start

from pyrbmi import RBMIDataset, Imputer, Strategy, pool

# 1. Load your ADaM-compatible longitudinal dataset
ds = RBMIDataset.from_dataframe(
    df,
    subject="USUBJID",
    treatment="TRT01A",
    visit="AVISIT",
    outcome="AVAL",
    baseline="BASE",
    reference_arm="Placebo",
)

# 2. Define imputation strategy (ICH E9R1 intercurrent event handling)
strategy = Strategy.jump_to_reference()

# 3. Run Bayesian multiple imputation
imputer = Imputer.bayesian(n_samples=200, n_chains=4)
imputed_datasets = imputer.fit_impute(ds, strategy=strategy)

# 4. Analyse each imputed dataset and pool via Rubin's rules
results = pool(imputed_datasets, estimand="difference_in_means")
print(results.summary())

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Features

Capability	Status
Bayesian MI (MCMC)	✅ v0.1.0
MAR strategy	✅ v0.1.0
Rubin's rules pooling	✅ v0.1.0
Approximate Bayesian MI	✅ v0.2.0
Jump-to-Reference (J2R)	✅ v0.2.0
Copy Reference (CR)	✅ v0.2.0
Copy Increment from Reference (CIN)	✅ v0.2.0
Last Mean Carried Forward (LMCF)	✅ v0.2.0
Conditional Mean Imputation (CMI)	✅ v0.3.0

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Validation

Numerical output parity is verified against R rbmi on reference datasets. See Validation → R Parity Report for details.

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References

• Gower-Page C, et al. (2022). rbmi: A R package for standard and reference based multiple imputation. Journal of Open Source Software, 7(78), 4251. https://doi.org/10.21105/joss.04251
• ICH E9(R1) (2019). Statistical Principles for Clinical Trials: Addendum on Estimands and Sensitivity Analysis. Step 4, November 2019.