clinipub 0.1.3

Transform clinical dataframes into publication-ready, beautifully styled tables for medical journals and manuscripts.

clinipub

A lightweight Python toolkit that turns clinical pandas.DataFrame datasets into publication-ready Table 1 summaries and missing-data audits.

clinipub automates clinical descriptive analytics, standard test selection, and publication-quality output for medical research manuscripts, registries, and regulatory reporting.

Why this package exists

Baseline tables and missingness audits are essential in clinical research, but most teams still prepare them manually. clinipub delivers a reproducible, data-first workflow for clinical scientists and medical writers.

Key features

• MissingDataAuditor: compute missingness, generate a styled HTML report, and run Little's MCAR test
• ClinicalDataAuditor: detect categorical vs continuous variables and test continuous normality
• BivariateTestSelector: select the appropriate clinical test automatically for continuous and categorical comparisons
• TableOneAssembler: assemble a stratified Table 1 with descriptive statistics and p-values

Installation

Install from PyPI:

pip install clinipub

Install from source for development:

git clone https://github.com/arsalananwar11/clinipub.git
cd clinipub
uv sync

Quick start

import pandas as pd
from clinipub import (
    MissingDataAuditor,
    ClinicalDataAuditor,
    BivariateTestSelector,
    TableOneAssembler,
)

# Load clinical data

df = pd.read_csv("baseline.csv")

# Audit missing data and run Little's MCAR test
missing_auditor = MissingDataAuditor(df)
missing_df = missing_auditor.calculate_missingness()
mcar_results = missing_auditor.run_mcar_test()

html_report = missing_auditor.to_html_report(
    audit_df=missing_df,
    thresholds={"low": 1.0, "mid": 20.0},
)

# Detect variable types and assess normality
auditor = ClinicalDataAuditor(df)
var_types = auditor.detect_variable_types()
normality = auditor.test_normality(var_types["continuous"])

# Select and execute bivariate tests
selector = BivariateTestSelector(df, stratify_by="treatment")
continuous_result = selector.test_continuous("age", is_normal=normality["age"])
cat_result = selector.test_categorical("smoker_status")

# Assemble publication-ready Table 1
assembler = TableOneAssembler(df, stratify_by="treatment")
table1 = assembler.build()

API overview

• MissingDataAuditor(data: pd.DataFrame)

  • calculate_missingness() → DataFrame with missing_count and missing_percentage
  • to_html_report(audit_df: pd.DataFrame = None, thresholds: dict = {'low': 5.0, 'mid': 30.0}) → styled HTML output
  • run_mcar_test() → dict with statistic, p_value, and degrees_of_freedom

• ClinicalDataAuditor(data: pd.DataFrame)

  • detect_variable_types(max_categorical_threshold: int = 10) → {'categorical': [...], 'continuous': [...]}
  • test_normality(continuous_cols: list, alpha: float = 0.05) → dict mapping column names to booleans

• BivariateTestSelector(data: pd.DataFrame, stratify_by: str)

  • test_continuous(col: str, is_normal: bool) → {'p_value': float, 'test': str}
  • test_categorical(col: str) → {'p_value': float, 'test': str}

• TableOneAssembler(data: pd.DataFrame, stratify_by: str, columns: list = None)

  • build() → styled pandas.DataFrame with stratified descriptive statistics and p-values

Developer workflow

Run tests with:

uv run pytest

References

• Little, R. J. A. (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association.
• CDISC, STROBE, and medical publication best practices for clinical summary reporting.

License

MIT License.

Contributing

Contributions are welcome. Please read CONTRIBUTING.md for issue and pull request guidelines.