def report_CEA_CBA(sim_outcomes_none, sim_outcomes_anticoag):
""" performs cost-effectiveness and cost-benefit analyses
:param sim_outcomes_none: outcomes of a cohort simulated under no therapy
:param sim_outcomes_anticoag: outcomes of a cohort simulated under anticoagultation therapy
"""
# define two strategies
no_therapy_strategy = Econ.Strategy(
name='No Therapy',
cost_obs=sim_outcomes_none.costs,
effect_obs=sim_outcomes_none.utilities,
color='red'
)
anticoag_therapy_strategy = Econ.Strategy(
name='Anticoagulation Therapy',
cost_obs=sim_outcomes_anticoag.costs,
effect_obs=sim_outcomes_anticoag.utilities,
color='blue'
)
# do cost-effectiveness analysis
# (the first strategy in the list of strategies is assumed to be the 'Base' strategy)
CEA = Econ.CEA(
strategies=[no_therapy_strategy, anticoag_therapy_strategy],
if_paired=False
)
# plot cost-effectiveness figure
CEA.plot_CE_plane(
title='Cost-Effectiveness Analysis',
x_label='Additional QALYs',
y_label='Additional Cost',
x_range=(-0.6, 1.5),
y_range=(-5000, 50000),
interval_type='c'
)
# report the CE table
CEA.build_CE_table(
interval_type='c',
alpha=D.ALPHA,
cost_digits=0,
effect_digits=2,
icer_digits=2)
# cost-benefit analysis
CBA = Econ.CBA(
strategies=[no_therapy_strategy, anticoag_therapy_strategy],
wtp_range=[0, 100000],
if_paired=False
)
# show the net monetary benefit figure
CBA.plot_incremental_nmbs(
title='Cost-Benefit Analysis',
x_label='Willingness-to-pay per QALY ($)',
y_label='Incremental Net Monetary Benefit ($)',
interval_type='c',
show_legend=True,
figure_size=(6, 5)
)
def report_CEA_CBA(multi_cohort_outcomes_mono, multi_cohort_outcomes_combo):
""" performs cost-effectiveness and cost-benefit analyses
:param multi_cohort_outcomes_mono: outcomes of a multi-cohort simulated under mono therapy
:param multi_cohort_outcomes_combo: outcomes of a multi-cohort simulated under combination therapy
"""
# define two strategies
mono_therapy_strategy = Econ.Strategy(
name='Mono Therapy',
cost_obs=multi_cohort_outcomes_mono.meanCosts,
effect_obs=multi_cohort_outcomes_mono.meanQALYs,
color='green')
combo_therapy_strategy = Econ.Strategy(
name='Combination Therapy',
cost_obs=multi_cohort_outcomes_combo.meanCosts,
effect_obs=multi_cohort_outcomes_combo.meanQALYs,
color='blue')
# do CEA
CEA = Econ.CEA(strategies=[mono_therapy_strategy, combo_therapy_strategy],
if_paired=True)
# show the cost-effectiveness plane
CEA.plot_CE_plane(title='Cost-Effectiveness Analysis',
x_label='Additional Discounted QALY',
y_label='Additional Discounted Cost',
fig_size=(6, 5),
add_clouds=True,
transparency=0.2)
# report the CE table
CEA.build_CE_table(
interval_type='p', # uncertainty (projection) interval
alpha=D.ALPHA,
cost_digits=0,
effect_digits=2,
icer_digits=2,
file_name='CETable.csv')
# CBA
NBA = Econ.CBA(strategies=[mono_therapy_strategy, combo_therapy_strategy],
wtp_range=(0, 50000),
if_paired=True)
# show the net monetary benefit figure
NBA.plot_incremental_nmbs(
title='Cost-Benefit Analysis',
x_label='Willingness-To-Pay for One Additional QALY ($)',
y_label='Incremental Net Monetary Benefit ($)',
interval_type='p',
show_legend=True,
figure_size=(6, 5),
)
def report_CEA_CBA(sim_outcomes_none, sim_outcomes_anti):
""" performs cost-effectiveness and cost-benefit analyses
:param sim_outcomes_mono: outcomes of a cohort simulated under mono therapy
:param sim_outcomes_combo: outcomes of a cohort simulated under combination therapy
"""
# define two strategies
no_therapy_strategy = Econ.Strategy(name='No Anticoagulation ',
cost_obs=sim_outcomes_none.costs,
effect_obs=sim_outcomes_none.utilities,
color='green')
anti_therapy_strategy = Econ.Strategy(
name='With Anticoagulation',
cost_obs=sim_outcomes_anti.costs,
effect_obs=sim_outcomes_anti.utilities,
color='blue')
# do CEA
CEA = Econ.CEA(strategies=[no_therapy_strategy, anti_therapy_strategy],
if_paired=False)
# plot cost-effectiveness figure
CEA.plot_CE_plane(title='Cost-Effectiveness Analysis',
x_label='Additional QALYs',
y_label='Additional Cost',
interval_type='c',
x_range=(-0.5, 1),
y_range=(-1000, 10000))
# report the CE table
CEA.build_CE_table(interval_type='c',
alpha=D.ALPHA,
cost_digits=0,
effect_digits=2,
icer_digits=2,
file_name='CETable.csv')
# CBA
NBA = Econ.CBA(strategies=[no_therapy_strategy, anti_therapy_strategy],
wtp_range=[0, 50000],
if_paired=False)
# show the net monetary benefit figure
NBA.plot_incremental_nmbs(title='Cost-Benefit Analysis',
x_label='Willingness-to-pay per QALY ($)',
y_label='Incremental Net Monetary Benefit ($)',
interval_type='c',
show_legend=True,
figure_size=(6, 5))
from SimPy import EconEval as EV
S0 = EV.Strategy(name='Base', cost_obs=[100], effect_obs=[1])
S1 = EV.Strategy(name='A1', cost_obs=[800], effect_obs=[0.5])
S2 = EV.Strategy(name='A2', cost_obs=[2000], effect_obs=[10])
S3 = EV.Strategy(name='A3', cost_obs=[500], effect_obs=[7])
S4 = EV.Strategy(name='A4', cost_obs=[-100], effect_obs=[2])
S5 = EV.Strategy(name='A5', cost_obs=[200], effect_obs=[2])
S6 = EV.Strategy(name='A6', cost_obs=[1000], effect_obs=[7.2])
S7 = EV.Strategy(name='A7', cost_obs=[1100], effect_obs=[7.3])
cea = EV.CEA(strategies=[S0, S1, S2, S3, S4, S5, S6, S7],
if_paired=False,
health_measure='u')
# show the ce plane:
cea.plot_CE_plane()
# build table
cea.build_CE_table(interval_type='n')
print('On frontier')
frontier_strategies = cea.get_strategies_on_frontier()
for i, s in enumerate(frontier_strategies):
print(s.name)
if i > 0:
print('incCost:', s.incCost.get_mean())
print('incEffect:', s.incEffect.get_mean())
print('ICER:', s.icer.get_ICER())
def build_CE_curve(self,
save_cea_results=False,
interval_type='n',
effect_multiplier=1,
cost_multiplier=1,
switch_cost_effect_on_figure=False,
wtp_range=None):
# cost-effectiveness analysis
self.CEA = Econ.CEA(self.strategies,
if_paired=True,
health_measure='d')
# CBA
if wtp_range is not None:
self.CBA = Econ.CBA(self.strategies,
wtp_range=wtp_range,
if_paired=True,
health_measure='d')
# if to save the results of the CEA
if save_cea_results:
self.CEA.build_CE_table(interval_type=interval_type,
file_name='CEA Table-' + self.name +
'.csv',
cost_multiplier=cost_multiplier,
effect_multiplier=effect_multiplier,
effect_digits=0)
# if the CE frontier should be calculated
if self.ifFindFrontier:
# find the (x, y)'s of strategies on the frontier
for idx, strategy in enumerate(
self.CEA.get_strategies_on_frontier()):
if switch_cost_effect_on_figure:
self.frontierXValues.append(strategy.dCost.get_mean() *
cost_multiplier)
self.frontierYValues.append(strategy.dEffect.get_mean() *
effect_multiplier)
else:
self.frontierXValues.append(strategy.dEffect.get_mean() *
effect_multiplier)
self.frontierYValues.append(strategy.dCost.get_mean() *
cost_multiplier)
self.frontierLabels.append(strategy.name)
if interval_type != 'n':
effect_interval = strategy.dEffect.get_interval(
interval_type=interval_type,
alpha=ALPHA,
multiplier=effect_multiplier)
cost_interval = strategy.dCost.get_interval(
interval_type=interval_type,
alpha=ALPHA,
multiplier=effect_multiplier)
if switch_cost_effect_on_figure:
self.frontierYIntervals.append(effect_interval)
self.frontierXIntervals.append(cost_interval)
else:
self.frontierXIntervals.append(effect_interval)
self.frontierYIntervals.append(cost_interval)
#else: # the CE frontier needs not to be calculated
# find the (x, y) values of strategies to display on CE plane
# remove the base strategy
if not self.xyLabelsProvided:
self.xyLabels = []
for strategy in [s for s in self.CEA.strategies if s.idx > 0]:
if switch_cost_effect_on_figure:
self.allDeltaEffects = np.append(
self.allDeltaEffects,
strategy.dEffectObs * effect_multiplier)
self.allDeltaCosts = np.append(
self.allDeltaCosts, strategy.dCostObs * cost_multiplier)
self.xValues.append(strategy.dCost.get_mean() *
cost_multiplier)
self.yValues.append(strategy.dEffect.get_mean() *
effect_multiplier)
self.xValuesByScenario.append(strategy.dCostObs *
cost_multiplier)
self.yValuesByScenario.append(strategy.dEffectObs *
effect_multiplier)
else:
self.allDeltaEffects = np.append(
self.allDeltaEffects,
strategy.effectObs * effect_multiplier)
self.allDeltaCosts = np.append(
self.allDeltaCosts, strategy.costObs * cost_multiplier)
self.xValues.append(strategy.dEffect.get_mean() *
effect_multiplier)
self.yValues.append(strategy.dCost.get_mean() *
cost_multiplier)
self.xValuesByScenario.append(strategy.dEffectObs *
effect_multiplier)
self.yValuesByScenario.append(strategy.dCostObs *
cost_multiplier)
if not self.xyLabelsProvided:
self.xyLabels.append(strategy.label)
if interval_type != 'n':
effect_interval = strategy.dEffect.get_interval(
interval_type=interval_type,
alpha=ALPHA,
multiplier=effect_multiplier)
cost_interval = strategy.dCost.get_interval(
interval_type=interval_type,
alpha=ALPHA,
multiplier=cost_multiplier)
# print(strategy.name, cost_interval, effect_interval)
if switch_cost_effect_on_figure:
self.yIntervals.append(effect_interval)
self.xIntervals.append(cost_interval)
else:
self.xIntervals.append(effect_interval)
self.yIntervals.append(cost_interval)
s_center = np.array([[10000, 0.2], [20000, 0.7], [50000, 1.2]])
s0 = ce.Strategy("s0",
s_center[0, 0] + np.random.normal(0, 1000, N),
s_center[0, 1] + np.random.normal(0, 0.01, N),
color='red')
s1 = ce.Strategy("s1",
s_center[1, 0] + np.random.normal(0, 1000, N),
s_center[1, 1] + np.random.normal(0, 0.01, N),
color='blue')
s2 = ce.Strategy("s2",
s_center[2, 0] + np.random.normal(0, 1000, N),
s_center[2, 1] + np.random.normal(0, 0.05, N),
color='green')
cea = ce.CEA([s0, s1, s2], if_paired=True)
cea.plot_CE_plane()
nmb_paired = ce.CBA([s0, s1, s2], wtp_range=[0, 100000], if_paired=True)
nmb_indp = ce.CBA([s0, s1, s2], wtp_range=[0, 100000],
if_paired=False) # list of frontier strategies as input
# Try NMB_Lines figure - paired CI
nmb_paired.plot_incremental_nmbs(title="deltaNMB lines for paired CI",
x_label="wtp values",
y_label="NMB values",
y_axis_multiplier=0.1,
interval_type='c',
show_legend=True,
figure_size=(6, 5))
s_center[3, 1] + np.random.normal(0, 200, 10))
s4 = ce.Strategy("s5", s_center[4, 0] + np.random.normal(0, 200, 10),
s_center[4, 1] + np.random.normal(0, 200, 10))
s5 = ce.Strategy("s6", s_center[5, 0] + np.random.normal(0, 200, 10),
s_center[5, 1] + np.random.normal(0, 200, 10))
s6 = ce.Strategy("s7", s_center[6, 0] + np.random.normal(0, 200, 10),
s_center[6, 1] + np.random.normal(0, 200, 10))
s7 = ce.Strategy("s8", s_center[7, 0] + np.random.normal(0, 200, 10),
s_center[7, 1] + np.random.normal(0, 200, 10))
s8 = ce.Strategy("s9", s_center[8, 0] + np.random.normal(0, 200, 10),
s_center[8, 1] + np.random.normal(0, 200, 10))
s9 = ce.Strategy("s10", s_center[9, 0] + np.random.normal(0, 200, 10),
s_center[9, 1] + np.random.normal(0, 200, 10))
# create a CEA object -- unpaired
myCEA = ce.CEA([s0, s1, s2, s3, s4, s5, s6, s7, s8, s9], if_paired=False)
# plot with label and sample cloud
myCEA.plot_CE_plane('CE plane with unpaired observations and showing labels',
x_label='E[Effect]',
y_label='E[Cost]',
show_legend=True,
add_clouds=True,
fig_size=(6, 6))
# table
print('')
myCEA.build_CE_table(interval_type='c',
cost_digits=0,
effect_digits=0,
icer_digits=1,