How to Perform Intent-to-Treat Analysis in Python

import pandas as pd
import numpy as np


def intent_to_treat_analysis(data):
    """Perform intent-to-treat (ITT) analysis.

    ITT compares outcomes based on initial treatment assignment,
    regardless of whether participants actually received the treatment.
    """
    # Create a copy to avoid mutating the original dataframe
    df = data.copy()

    # In ITT, we analyze based on the assigned group, not actual treatment received
    # Here we use 'assigned_treatment' column as the ITT variable

    # Calculate mean outcome for each assigned group
    itt_results = df.groupby('assigned_treatment')['outcome'].agg(['mean', 'std', 'count'])

    # Calculate the ITT effect (difference in means)
    treatment_mean = itt_results.loc[1, 'mean']
    control_mean = itt_results.loc[0, 'mean']
    itt_effect = treatment_mean - control_mean

    # Perform a simple t-test for statistical significance
    from scipy import stats
    treatment_outcomes = df[df['assigned_treatment'] == 1]['outcome']
    control_outcomes = df[df['assigned_treatment'] == 0]['outcome']
    t_stat, p_value = stats.ttest_ind(treatment_outcomes, control_outcomes)

    return {
        'group_summary': itt_results,
        'itt_effect': itt_effect,
        't_statistic': t_stat,
        'p_value': p_value
    }


if __name__ == "__main__":
    # Mock data: 10 participants
    mock_data = pd.DataFrame({
        'participant_id': range(1, 11),
        'assigned_treatment': [1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
        'outcome': [85, 78, 92, 70, 88, 65, 72, 68, 60, 75]
    })

    result = intent_to_treat_analysis(mock_data)

    print("Intent-to-Treat Analysis Results")
    print("=" * 35)
    print("\nGroup Summary:")
    print(result['group_summary'])
    print(f"\nITT Effect: {result['itt_effect']:.2f}")
    print(f"T-Statistic: {result['t_statistic']:.2f}")
    print(f"P-Value: {result['p_value']:.4f}")