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Project: Hypothesis Testing in Healthcare
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  • Hypothesis Testing in Healthcare: Drug Safety

    A pharmaceutical company GlobalXYZ has just completed a randomized controlled drug trial. To promote transparency and reproducibility of the drug's outcome, they (GlobalXYZ) have presented the dataset to your organization, a non-profit that focuses primarily on drug safety.

    The dataset provided contained five adverse effects, demographic data, vital signs, etc. Your organization is primarily interested in the drug's adverse reactions. It wants to know if the adverse reactions, if any, are of significant proportions. It has asked you to explore and answer some questions from the data.

    The dataset drug_safety.csv was obtained from Hbiostat courtesy of the Vanderbilt University Department of Biostatistics. It contained five adverse effects: headache, abdominal pain, dyspepsia, upper respiratory infection, chronic obstructive airway disease (COAD), demographic data, vital signs, lab measures, etc. The ratio of drug observations to placebo observations is 2 to 1.

    For this project, the dataset has been modified to reflect the presence and absence of adverse effects adverse_effects and the number of adverse effects in a single individual num_effects.

    The columns in the modified dataset are:

    ColumnDescription
    sexThe gender of the individual
    ageThe age of the individual
    weekThe week of the drug testing
    trxThe treatment (Drug) and control (Placebo) groups
    wbcThe count of white blood cells
    rbcThe count of red blood cells
    adverse_effectsThe presence of at least a single adverse effect
    num_effectsThe number of adverse effects experienced by a single individual

    The original dataset can be found here

    # Import packages
    import numpy as np
    import pandas as pd
    from scipy.stats import norm
    from statsmodels.stats.proportion import proportions_ztest
    import pingouin
    import seaborn as sns
    import matplotlib.pyplot as plt
    
    # Load the dataset
    drug_safety = pd.read_csv("drug_safety.csv")
    display(drug_safety)
    # Create a count plot to show the number of adverse effects (num_effects) in the Drug and Placebo groups
    
    plot_num_eff=sns.countplot(data=drug_safety, x='num_effects', hue='trx')
    plt.xlabel('Number of Adverse Effects')
    plt.title('Distribution of the Number of Effects Between the Groups')
    plt.show()
    # get number of observations in each group
    trx_num = drug_safety.groupby('trx')['adverse_effects'].value_counts()
    
    # get number of total observations
    trx_total = drug_safety.groupby('trx')['adverse_effects'].count()
    
    # print pivot tables 
    print(trx_num)
    print(trx_total)
    
    # storing ztest required inputs in variables and printing
    sux=[trx_num[('Drug', 'Yes')], trx_num[('Placebo', 'Yes')]]
    cnts=[trx_total['Drug'], trx_total['Placebo']]
    print(sux)
    print(cnts)
    # performing proportions ztest
    two_samp_z_stat,two_samp_z_p_value = proportions_ztest(count=sux, nobs=cnts, alternative='two-sided')
    
    # rounding result to 3 decimal places
    two_samp_z_stat = two_samp_z_stat.round(decimals=3)
    two_samp_z_p_value = two_samp_z_p_value.round(decimals=3)
    
    # printing results
    print(two_samp_z_stat)
    print(two_samp_z_p_value)
    # performing chi-square test of independence 
    expected, observed, stats = pingouin.chi2_independence(data=drug_safety,x='trx',y='num_effects')
    
    stats = stats.round(3)
    print(stats)
    
    
    # storing p_value of the test
    pearson_num_effect_trx = stats[stats['test'] == 'pearson']
    print(pearson_num_effect_trx)
    # visualizing distribution of age in the Drug and Placebo groups.
    sns.histplot(data=drug_safety, x='age', hue='trx')
    plt.show()
    # subsetting ages of drug and placebo receivers
    age_drug = drug_safety.loc[drug_safety["trx"] == "Drug", "age"]
    age_placebo = drug_safety.loc[drug_safety["trx"] == "Placebo", "age"]
    
    display(age_drug)
    display(age_placebo)
    # storing p-value in a variable
    two_samp_ind_results = pingouin.mwu(age_drug, age_placebo).round(3)
    display(two_ind_samp_results)