Clinical Trial Drug Response ANOVA

import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats

np.random.seed(42)

# Clinical trial data: response scores (0-100) for three drug treatments
placebo = np.random.normal(45, 15, 120)
drug_a = np.random.normal(62, 12, 120)
drug_b = np.random.normal(71, 14, 120)

# ANOVA test
F_stat, p_value = stats.f_oneway(placebo, drug_a, drug_b)

# Create figure with dark theme
fig, ax = plt.subplots(figsize=(12, 7), facecolor='#0a0a0f')
ax.set_facecolor('#0a0a0f')

# Colors from neon palette
colors = ['#F5276C', '#27D3F5', '#6CF527']

# Create violin plot
parts = ax.violinplot([placebo, drug_a, drug_b], positions=[1, 2, 3], 
                       showmeans=True, showmedians=True)

# Style the violins
for i, pc in enumerate(parts['bodies']):
    pc.set_facecolor(colors[i])
    pc.set_alpha(0.7)
    pc.set_edgecolor(colors[i])
    pc.set_linewidth(2)

# Style other elements
parts['cmeans'].set_color('#F5D327')
parts['cmeans'].set_linewidth(2)
parts['cmedians'].set_color('white')
parts['cmedians'].set_linewidth(1.5)
for partname in ['cbars', 'cmins', 'cmaxes']:
    parts[partname].set_color('#666666')
    parts[partname].set_linewidth(1)

# Add statistical annotation box - moved to bottom left
stats_text = f"One-Way ANOVA Results\n" + "─" * 22 + f"\nF-statistic: {F_stat:.2f}\np-value: {p_value:.2e}"
bbox_props = dict(boxstyle="round,pad=0.5", facecolor='#1a1a2e', edgecolor='#27D3F5', alpha=0.9)
ax.text(0.02, 0.02, stats_text, transform=ax.transAxes, fontsize=11, color='white',
        verticalalignment='bottom', fontfamily='monospace', bbox=bbox_props)

# Add mean annotations - positioned to the right of violins
means = [placebo.mean(), drug_a.mean(), drug_b.mean()]
labels = ['Placebo', 'Drug A', 'Drug B']
for i, (mean, label) in enumerate(zip(means, labels)):
    ax.annotate(f'μ = {mean:.1f}', xy=(i+1, mean), xytext=(i+1.25, mean),
                fontsize=10, color=colors[i], fontweight='bold',
                arrowprops=dict(arrowstyle='->', color=colors[i], lw=1.5))

# Significance brackets
if p_value < 0.05:
    ax.plot([1, 1, 3, 3], [98, 102, 102, 98], 'w-', lw=1.5)
    ax.text(2, 104, '***' if p_value < 0.001 else '**' if p_value < 0.01 else '*',
            ha='center', color='#F5D327', fontsize=14, fontweight='bold')

# Styling
ax.set_xticks([1, 2, 3])
ax.set_xticklabels(labels, fontsize=12, color='white')
ax.set_ylabel('Treatment Response Score', fontsize=12, color='white', fontweight='500')
ax.set_title('Clinical Trial: Drug Efficacy Comparison\nPhase III Randomized Controlled Trial', 
             fontsize=14, color='white', fontweight='bold', pad=20)

ax.tick_params(colors='#888888', labelsize=10)
for spine in ax.spines.values():
    spine.set_color('#333333')
ax.yaxis.grid(True, color='#1a1a2e', linewidth=0.5, alpha=0.5)
ax.set_axisbelow(True)
ax.set_ylim(0, 115)

plt.tight_layout()
plt.show()