def simulate_treatment_success(success_rate, num_simulations=10000):
"""
Simulate the treatment success over a number of trials.
:param success_rate: The probability of the treatment working (between 0 and 1).
:param num_simulations: Number of test runs to simulate.
:return: Probability of treatment working based on simulations.
"""
results = np.random.rand(num_simulations) < success_rate
success_probability = np.mean(results)
return success_probability
def run_simulation_for_cancer_stages():
Define the success rates for each stage
success_rates = {
"Stage 1": np.random.uniform(0.7, 0.9),
"Stage 2": np.random.uniform(0.5, 0.7),
"Stage 3": np.random.uniform(0.3, 0.5),
"Stage 4": np.random.uniform(0.1, 0.3)
}
num_simulations = 10000 # Number of simulations to run for each stage
results = {}
for stage, success_rate in success_rates.items():
success_probability = simulate_treatment_success(success_rate, num_simulations)
results[stage] = success_probability
return results
Run the simulation
results = run_simulation_for_cancer_stages()
Output the results
for stage, probability in results.items():
print(f"Estimated probability of treatment success for {stage}: {probability:.2f}")
import numpy as np
def simulate_treatment_success(success_rate, num_simulations=10000): """ Simulate the treatment success over a number of trials.
def run_simulation_for_cancer_stages():
Define the success rates for each stage
Run the simulation
results = run_simulation_for_cancer_stages()
Output the results
for stage, probability in results.items(): print(f"Estimated probability of treatment success for {stage}: {probability:.2f}")