import SimPy.EconEval as EconEval
np.random.seed(573)
cost_base = np.random.normal(loc=10000, scale=100, size=1000)
effect_base = np.random.normal(loc=1, scale=.1, size=1000)
cost_intervention = np.random.normal(loc=20000, scale=200, size=1000)
effect_intervention = np.random.normal(loc=2, scale=.2, size=1000)
print('')
# ICER calculation assuming paired observations
ICER_paired = EconEval.ICER_Paired(name='Testing paired ICER',
costs_new=cost_intervention,
effects_new=effect_intervention,
costs_base=cost_base,
effects_base=effect_base)
print('Paired ICER:'
'\n\tICER: {} '
'\n\tCI (boostrap): {} '
'\n\tCI (Bayesian): {} '
'\n\tPI: {}'.format(ICER_paired.get_ICER(), ICER_paired.get_CI(0.05),
ICER_paired.get_CI(0.05, method='Bayesian'),
ICER_paired.get_PI(0.05)))
# ICER calculation assuming independent observations
ICER_indp = EconEval.ICER_Indp('Testing independent ICER',
costs_new=cost_intervention,
effects_new=effect_intervention,
costs_base=cost_base,
import SimPy.EconEval as EconEval
np.random.seed(573)
cost_base = np.random.normal(loc=10000, scale=100, size=1000)
effect_base = np.random.normal(loc=2, scale=.1, size=1000)
cost_intervention = np.random.normal(loc=20000, scale=200, size=1000)
effect_intervention = np.random.normal(loc=1, scale=.2, size=1000)
print('')
# ICER calculation assuming paired observations
ICER_paired = EconEval.ICER_Paired('Testing paired ICER',
cost_intervention,
effect_intervention,
cost_base,
effect_base,
health_measure='d')
print(
'Paired ICER:\n\tICER: {} \n\tCI (boostrap): {} \n\tCI (Bayesian): {} \n\tPI: '
.format(ICER_paired.get_ICER(), ICER_paired.get_CI(0.05, 1000),
ICER_paired.get_CI(0.05, 1000, method='Bayesian'),
ICER_paired.get_PI(0.05)))
# ICER calculation assuming independent observations
ICER_indp = EconEval.ICER_Indp('Testing independent ICER',
cost_intervention,
effect_intervention,
cost_base,
effect_base,
health_measure='d')
