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:
| Column | Description |
|---|---|
sex | The gender of the individual |
age | The age of the individual |
week | The week of the drug testing |
trx | The treatment (Drug) and control (Placebo) groups |
wbc | The count of white blood cells |
rbc | The count of red blood cells |
adverse_effects | The presence of at least a single adverse effect |
num_effects | The number of adverse effects experienced by a single individual |
The original dataset can be found here.
Your organization has asked you to explore and answer some questions from the data collected. See the project instructions.
# Import packages
import numpy as np
import pandas as pd
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")
# Start coding here...
#Grouping the treatment (drug/placebo) by the adverse effect (Yes/No)
adv_eff_trx = drug_safety.groupby("trx")["adverse_effects"].value_counts()
#Getting the number of samples for each treatment(drug/placebo)
trx_num = drug_safety["trx"].value_counts()
#creating an array of yes for the treatment (drug/placebo)
yes_trx = [adv_eff_trx["Drug"]["Yes"],adv_eff_trx["Placebo"]["Yes"]]
# creating an array of either yes/no for each treatment
num_group = [trx_num["Drug"], trx_num["Placebo"]]
# the p_value of the test
two_sample_p_value = proportions_ztest(yes_trx,num_group)[1]
#test if num_effects is independent of trx
num_effect_group = pingouin.chi2_independence(data=drug_safety, x="num_effects", y="trx")
num_effects_p_value = num_effect_group[2]["pval"][0]
# Create a histogram with Seaborn
sns.histplot(data=drug_safety, x="age", hue="trx")
#Checking for normality to decided whether to use a parametric or non-parametric test.
normality = pingouin.normality(
data=drug_safety,
dv='age',
group='trx',
method='shapiro', # the default
alpha=0.05) # 0.05 is also the default
#Ages of the samples with drug
age_drug = drug_safety.loc[drug_safety["trx"] == "Drug", "age"]
#Ages of the samples with Placebo
age_placebo = drug_safety.loc[drug_safety["trx"] == "Placebo", "age"]
#Since the distribution is not normal
#Conduct a two-sided Mann-Whitney U test
age_group_effect = pingouin.mwu(age_drug, age_placebo)
#Extracting the p-value
age_group_effects_p_value = age_group_effect["p-val"]
age_group_effects_p_value