def test_upper_bounds(self):
highBPPerson = Person(
age=75,
gender=0,
raceEthnicity=1,
sbp=500,
ldl=90,
trig=150,
dbp=80,
a1c=6.5,
hdl=50,
totChol=210,
bmi=22,
waist=50,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=1,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0,
initializeAfib=initializeAfib,
)
highBPPerson.advance_risk_factors(self._risk_model_repository)
self.assertEqual(300, highBPPerson._sbp[-1])
def setUp(self):
self._test_person = Person(
age=75,
gender=0,
raceEthnicity=1,
sbp=140,
dbp=80,
a1c=6.5,
hdl=50,
totChol=210,
ldl=90,
trig=150,
bmi=22,
waist=50,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=1,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0,
initializeAfib=initializeAfib,
)
self._risk_model_repository = TestRiskModelRepository()
class TestNHANESLinearRiskFactorModel(unittest.TestCase):
def setUp(self):
self._test_person = Person(
age=75,
gender=0,
raceEthnicity=1,
sbp=140,
dbp=80,
a1c=6.5,
hdl=50,
totChol=210,
ldl=90,
trig=150,
bmi=22,
waist=50,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=1,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0,
initializeAfib=initializeAfib,
)
self._risk_model_repository = TestRiskModelRepository()
def test_sbp_model(self):
self._test_person.advance_risk_factors(self._risk_model_repository)
expectedSBP = 75 * 1 + 140 * 0.5 + 80
self.assertEqual(expectedSBP, self._test_person._sbp[-1])
def test_upper_bounds(self):
highBPPerson = Person(
age=75,
gender=0,
raceEthnicity=1,
sbp=500,
ldl=90,
trig=150,
dbp=80,
a1c=6.5,
hdl=50,
totChol=210,
bmi=22,
waist=50,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=1,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0,
initializeAfib=initializeAfib,
)
highBPPerson.advance_risk_factors(self._risk_model_repository)
self.assertEqual(300, highBPPerson._sbp[-1])
def setUp(self):
self.joe = Person(42, NHANESGender.MALE,
NHANESRaceEthnicity.NON_HISPANIC_BLACK, 140, 90, 5.5,
50, 200, 25, 90, 150, 45, 0,
Education.COLLEGEGRADUATE, SmokingStatus.NEVER,
AlcoholCategory.NONE, 0, 0, 0, initializeAFib)
self._always_positive_repository = AlwaysPositiveOutcomeRepository()
self._always_negative_repository = AlwaysNegativeOutcomeRepository()
self.cvDeterminer = CVOutcomeDetermination(
self._always_positive_repository)
def setUp(self):
self.oldJoe = Person(
age=60,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_BLACK,
sbp=140,
dbp=90,
a1c=5.5,
hdl=50,
totChol=200,
bmi=25,
ldl=90,
trig=150,
waist=45,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0,
initializeAfib=initializeAFib,
)
self.youngJoe = Person(
age=40,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_BLACK,
sbp=140,
dbp=90,
a1c=5.5,
hdl=50,
totChol=200,
bmi=25,
ldl=90,
trig=150,
waist=45,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0,
initializeAfib=initializeAFib,
)
def setUp(self):
self.joe = Person(
42,
NHANESGender.MALE,
NHANESRaceEthnicity.NON_HISPANIC_BLACK,
140,
90,
5.5,
50,
200,
25,
90,
150,
70,
0,
Education.COLLEGEGRADUATE,
SmokingStatus.NEVER,
AlcoholCategory.NONE,
0,
0,
0,
0,
initializeAFib,
selfReportStrokeAge=None,
selfReportMIAge=None,
dfIndex=1,
diedBy2015=0,
)
def generate_starting_mean_patient(self):
df = self.get_people_initial_state_as_dataframe()
return Person(
age=int(round(df.age.mean())),
gender=NHANESGender(df.gender.mode()),
raceEthnicity=NHANESRaceEthnicity(df.raceEthnicity.mode()),
sbp=df.sbp.mean(),
dbp=df.dbp.mean(),
a1c=df.a1c.mean(),
hdl=df.hdl.mean(),
totChol=df.totChol.mean(),
bmi=df.bmi.mean(),
ldl=df.ldl.mean(),
trig=df.trig.mean(),
waist=df.waist.mean(),
anyPhysicalActivity=df.anyPhysicalActivity.mode(),
education=Education(df.education.mode()),
smokingStatus=SmokingStatus(df.smokingStatus.mode()),
antiHypertensiveCount=int(round(df.antiHypetensiveCount().mean())),
statin=df.statin.mode(),
otherLipidLoweringMedicationCount=int(
round(df.otherLipidLoweringMedicationCount.mean())),
initializeAfib=(lambda _: False),
selfReportStrokeAge=None,
selfReportMIAge=None,
randomEffects=self._outcome_model_repository.get_random_effects(),
)
def build_person(x, outcome_model_repository):
return Person(
age=x.age,
gender=NHANESGender(int(x.gender)),
raceEthnicity=NHANESRaceEthnicity(int(x.raceEthnicity)),
sbp=x.meanSBP,
dbp=x.meanDBP,
a1c=x.a1c,
hdl=x.hdl,
ldl=x.ldl,
trig=x.trig,
totChol=x.tot_chol,
bmi=x.bmi,
waist=x.waist,
anyPhysicalActivity=x.anyPhysicalActivity,
smokingStatus=SmokingStatus(int(x.smokingStatus)),
alcohol=AlcoholCategory.get_category_for_consumption(x.alcoholPerWeek),
education=Education(int(x.education)),
antiHypertensiveCount=x.antiHypertensive,
statin=x.statin,
otherLipidLoweringMedicationCount=x.otherLipidLowering,
creatinine=x.serumCreatinine,
initializeAfib=initializeAFib,
initializationRepository=InitializationRepository(),
selfReportStrokeAge=x.selfReportStrokeAge,
selfReportMIAge=np.random.randint(18, x.age)
if x.selfReportMIAge == 99999 else x.selfReportMIAge,
randomEffects=outcome_model_repository.get_random_effects(),
dfIndex=x.index,
diedBy2015=x.diedBy2015 == True,
)
def getPerson(self, age=65):
return Person(
age=age,
gender=0,
raceEthnicity=1,
sbp=140,
dbp=80,
a1c=6.5,
hdl=50,
totChol=210,
ldl=90,
trig=150,
bmi=22,
waist=50,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=1,
alcohol=0,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0,
initializeAfib=TestQALYAssignment.initializeAfib,
initializationRepository=InitializationRepository(),
)
def get_or_init_population_dataframe(cls):
if VectorizedTestFixture._population_dataframe is None:
test_person = Person(
age=71,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=144.667,
dbp=52.6667,
a1c=9.5,
hdl=34,
totChol=191,
bmi=30.05,
ldl=110.0,
trig=128,
waist=45,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.FORMER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0.6,
initializeAfib=(lambda _: None),
randomEffects={"gcp": 0},
)
base_gcp = GCPModel().get_risk_for_person(test_person)
test_person._gcp.append([base_gcp])
VectorizedTestFixture._population_dataframe = init_vectorized_population_dataframe(
[test_person])
return VectorizedTestFixture._population_dataframe
def build_person(x):
return Person(age=x.age,
gender=NHANESGender(int(x.gender)),
raceEthnicity=NHANESRaceEthnicity(int(x.raceEthnicity)),
sbp=x.meanSBP,
dbp=x.meanDBP,
a1c=x.a1c,
hdl=x.hdl,
ldl=x.ldl,
trig=x.trig,
totChol=x.tot_chol,
bmi=x.bmi,
waist=x.waist,
anyPhysicalActivity=x.anyPhysicalActivity,
smokingStatus=SmokingStatus(int(x.smokingStatus)),
alcohol=AlcoholCategory.get_category_for_consumption(
x.alcoholPerWeek),
education=Education(int(x.education)),
antiHypertensiveCount=x.antiHypertensive,
statin=x.statin,
otherLipidLoweringMedicationCount=x.otherLipidLowering,
initializeAfib=initializeAFib,
selfReportStrokeAge=x.selfReportStrokeAge,
selfReportMIAge=x.selfReportMIAge,
dfIndex=x.index,
diedBy2015=x.diedBy2015)
def setUp(self):
self.imputed_dataset_first_person = Person(71, NHANESGender.MALE,
NHANESRaceEthnicity.NON_HISPANIC_WHITE,
144.667, 52.6667, 9.5, 34, 191, 30.05,
110.0, 128, 45, 0, Education.COLLEGEGRADUATE,
SmokingStatus.FORMER, AlcoholCategory.NONE, 0, 0, 0,
initializeAFib)
model_spec = load_model_spec("nhanesMortalityModel")
self.model = StatsModelCoxModel(CoxRegressionModel(**model_spec))
def setUp(self):
self.baselineSBP = 140
self.baselineDBP = 80
self._test_person = Person(
age=75,
gender=0,
raceEthnicity=1,
sbp=self.baselineSBP,
dbp=self.baselineDBP,
a1c=6.5,
hdl=50,
totChol=210,
ldl=90,
trig=150,
bmi=22,
waist=50,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=1,
alcohol=0,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=TestTreatmentStrategy.initializeAfib)
self._risk_model_repository = TestRiskModelRepository()
# setup so that the SBP always stays the same
self._risk_model_repository._repository['sbp']._params['age'] = 0
self._risk_model_repository._repository['sbp']._params['sbp'] = 1.0
self._risk_model_repository._repository['sbp']._params['intercept'] = 0
self._risk_model_repository._repository['dbp']._params['age'] = 0
self._risk_model_repository._repository['dbp']._params['dbp'] = 1.0
self._risk_model_repository._repository['dbp']._params['sbp'] = 0
self._risk_model_repository._repository['dbp']._params['intercept'] = 0
# setup so that the anti-hypertensive count stays at zero
self._risk_model_repository._repository[
'antiHypertensiveCount']._params['age'] = 0
self._risk_model_repository._repository[
'antiHypertensiveCount']._params['sbp'] = 0
self._risk_model_repository._repository[
'antiHypertensiveCount']._params['intercept'] = 0
def setUp(self):
self.joe = Person(
42,
NHANESGender.MALE,
NHANESRaceEthnicity.NON_HISPANIC_BLACK,
140,
90,
5.5,
50,
200,
25,
90,
150,
45,
0,
Education.COLLEGEGRADUATE,
SmokingStatus.NEVER,
AlcoholCategory.NONE,
0,
0,
0,
0,
initializeAFib,
)
self.joe_with_mi = copy.deepcopy(self.joe)
self.joe_with_mi.add_outcome_event(Outcome(OutcomeType.MI, False))
self.joe_with_stroke = copy.deepcopy(self.joe)
self.joe_with_stroke.add_outcome_event(Outcome(OutcomeType.STROKE, False))
self._population_dataframe = init_vectorized_population_dataframe(
[self.joe, self.joe_with_mi, self.joe_with_stroke],
with_base_gcp=True,
)
self._always_positive_repository = AlwaysPositiveOutcomeRepository()
self._always_negative_repository = AlwaysNegativeOutcomeRepository()
self.cvDeterminer = CVOutcomeDetermination(self._always_positive_repository)
def get_risk_for_person(self,
person,
years=1,
vectorized=False,
test=False):
random_effect = 0
if not vectorized:
random_effect = person._randomEffects[
"gcp"] if "gcp" in person._randomEffects else 0
residual = 0 if test else np.random.normal(0.38, 6.99)
linPred = 0
if vectorized:
linPred = self.calc_linear_predictor_for_patient_characteristics(
yearsInSim=person.totalYearsInSim,
raceEthnicity=person.raceEthnicity,
gender=person.gender,
baseAge=person.baseAge,
education=person.education,
alcohol=person.alcoholPerWeek,
smokingStatus=person.smokingStatus,
bmi=person.bmi,
waist=person.waist,
totChol=person.totChol,
meanSBP=person.meanSbp,
anyAntiHpertensive=(person.antiHypertensiveCount > 0),
fastingGlucose=Person.convert_a1c_to_fasting_glucose(
person.a1c),
physicalActivity=person.anyPhysicalActivity,
afib=person.afib,
)
else:
linPred = self.calc_linear_predictor_for_patient_characteristics(
yearsInSim=person.years_in_simulation(),
raceEthnicity=person._raceEthnicity,
gender=person._gender,
baseAge=person._age[0],
education=person._education,
alcohol=person._alcoholPerWeek[-1],
smokingStatus=person._smokingStatus,
bmi=person._bmi[-1],
waist=person._waist[-1],
totChol=person._totChol[-1],
meanSBP=np.array(person._sbp).mean(),
anyAntiHpertensive=(person._antiHypertensiveCount[-1] > 0),
fastingGlucose=person.get_fasting_glucose(not test),
physicalActivity=person._anyPhysicalActivity[-1],
afib=person._afib[-1],
)
return linPred + random_effect + residual
def getPerson(self, baselineSBP=140, baselineDBP=80):
return Person(
age=75,
gender=0,
raceEthnicity=1,
sbp=baselineSBP,
dbp=baselineDBP,
a1c=6.5,
hdl=50,
totChol=210,
ldl=90,
trig=150,
bmi=22,
waist=50,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=1,
alcohol=0,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0.0,
initializeAfib=TestTreatmentStrategy.initializeAfib,
)
def setUp(self):
self._test_case_one = Person(
age=65 - 0.828576318 * 10,
gender=NHANESGender.FEMALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=120 + 0.45 * 10,
dbp=80,
# guessingon the centering standard for glucose...may have to check
a1c=Person.convert_fasting_glucose_to_a1c(100 - 1.1 * 10),
hdl=50,
totChol=127 - 3.64 * 10,
ldl=90,
trig=150,
bmi=26.6 + 15.30517532,
waist=94 + 19.3,
anyPhysicalActivity=1,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.ONETOSIX,
antiHypertensiveCount=1,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0,
initializeAfib=TestGCPModel.initializeAfib,
)
self._test_case_two = Person(
age=65 - 0.458555784 * 10,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=120 + 0.3 * 10,
dbp=80,
# guessingon the centering standard for glucose...may have to check
a1c=Person.convert_fasting_glucose_to_a1c(100 + 0.732746529 * 10),
hdl=50,
totChol=127 + 1.18 * 10,
ldl=90,
trig=150,
bmi=26.6 + 0.419305619,
waist=94 - 2.5,
anyPhysicalActivity=1,
education=Education.SOMEHIGHSCHOOL,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.ONETOSIX,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0,
initializeAfib=TestGCPModel.initializeAfib,
)
self._test_case_three = Person(
age=65 - 0.358692676 * 10,
gender=NHANESGender.FEMALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_BLACK,
sbp=120 + 2.3 * 10,
dbp=80,
# guessingon the centering standard for glucose...may have to check
a1c=Person.convert_fasting_glucose_to_a1c(100 + 0.8893 * 10),
hdl=50,
totChol=127 + 4.7769 * 10,
ldl=90,
trig=150,
bmi=26.6 + 2.717159247,
waist=94 + 9,
anyPhysicalActivity=1,
education=Education.LESSTHANHIGHSCHOOL,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=1,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0,
initializeAfib=TestGCPModel.initializeAfib,
)
self._test_case_one._randomEffects["gcp"] = 0
self._test_case_two._randomEffects["gcp"] = 0
self._test_case_three._randomEffects["gcp"] = 0
class TestPersonAdvanceOutcomes(unittest.TestCase):
def setUp(self):
self.joe = Person(
42,
NHANESGender.MALE,
NHANESRaceEthnicity.NON_HISPANIC_BLACK,
140,
90,
5.5,
50,
200,
25,
90,
150,
45,
0,
Education.COLLEGEGRADUATE,
SmokingStatus.NEVER,
AlcoholCategory.NONE,
0,
0,
0,
0,
initializeAFib,
)
self.joe_with_mi = copy.deepcopy(self.joe)
self.joe_with_mi.add_outcome_event(Outcome(OutcomeType.MI, False))
self.joe_with_stroke = copy.deepcopy(self.joe)
self.joe_with_stroke.add_outcome_event(Outcome(OutcomeType.STROKE, False))
self._population_dataframe = init_vectorized_population_dataframe(
[self.joe, self.joe_with_mi, self.joe_with_stroke],
with_base_gcp=True,
)
self._always_positive_repository = AlwaysPositiveOutcomeRepository()
self._always_negative_repository = AlwaysNegativeOutcomeRepository()
self.cvDeterminer = CVOutcomeDetermination(self._always_positive_repository)
def test_dead_is_dead_advance_year(self):
self.joe._alive[-1] = False
with self.assertRaises(RuntimeError, msg="Person is dead. Can not advance year"):
self.joe.advance_year(None, None)
def test_dead_is_dead_advance_risk_factors(self):
self.joe._alive[-1] = False
with self.assertRaises(RuntimeError, msg="Person is dead. Can not advance risk factors"):
self.joe.advance_risk_factors(None)
def test_dead_is_dead_advance_outcomes(self):
self.joe._alive[-1] = False
with self.assertRaises(RuntimeError, msg="Person is dead. Can not advance outcomes"):
self.joe.advance_outcomes(None)
def test_will_have_fatal_mi(self):
joe_data = self._population_dataframe.iloc[0]
is_max_prob_mi_fatal = self.cvDeterminer._will_have_fatal_mi(
joe_data,
vectorized=True,
overrideMIProb=1.0,
)
is_min_prob_mi_fatal = self.cvDeterminer._will_have_fatal_mi(
joe_data,
vectorized=True,
overrideMIProb=0.0,
)
self.assertTrue(is_max_prob_mi_fatal)
self.assertFalse(is_min_prob_mi_fatal)
def test_fatal_mi_secondary_prob(self):
self.cvDeterminer.mi_secondary_case_fatality = 1.0
self.cvDeterminer.mi_case_fatality = 0.0
joe_data = self._population_dataframe.iloc[0]
joe_with_mi_data = self._population_dataframe.iloc[1] # same as joe_data plus 1 MI
will_have_fatal_first_mi = self.cvDeterminer._will_have_fatal_mi(
joe_data,
vectorized=True,
overrideMIProb=0.0,
)
will_have_fatal_second_mi = self.cvDeterminer._will_have_fatal_mi(
joe_with_mi_data,
vectorized=True,
overrideMIProb=0.0,
)
self.assertFalse(will_have_fatal_first_mi)
# even though the passed fatality rate is zero, it should be overriden by the
# secondary rate given that joe had a prior MI
self.assertTrue(will_have_fatal_second_mi)
def test_fatal_stroke_secondary_prob(self):
self.cvDeterminer.stroke_secondary_case_fatality = 1.0
self.cvDeterminer.stroke_case_fatality = 0.0
joe_data = self._population_dataframe.iloc[0]
joe_with_stroke_data = self._population_dataframe.iloc[2] # same as joe_data plus 1 stroke
will_have_fatal_first_stroke = self.cvDeterminer._will_have_fatal_stroke(
joe_data,
vectorized=True,
overrideStrokeProb=0.0,
)
will_have_fatal_second_stroke = self.cvDeterminer._will_have_fatal_stroke(
joe_with_stroke_data,
vectorized=True,
overrideStrokeProb=0.0,
)
self.assertFalse(will_have_fatal_first_stroke)
# even though the passed fatality rate is zero, it shoudl be overriden by the
# secondary rate given that joeclone had a prior stroke
self.assertTrue(will_have_fatal_second_stroke)
def test_will_have_fatal_stroke(self):
joe_data = self._population_dataframe.iloc[0]
is_max_prob_stroke_fatal = self.cvDeterminer._will_have_fatal_stroke(
joe_data,
vectorized=True,
overrideStrokeProb=1.0,
)
is_min_prob_stroke_fatal = self.cvDeterminer._will_have_fatal_stroke(
joe_data,
vectorized=True,
overrideStrokeProb=0.0,
)
self.assertTrue(is_max_prob_stroke_fatal)
self.assertFalse(is_min_prob_stroke_fatal)
def test_has_mi_vs_stroke(self):
joe_data = self._population_dataframe.iloc[0]
has_mi_max_manual_prob = self.cvDeterminer._will_have_mi(
joe_data,
outcome_model_repository=None,
vectorized=False,
manualMIProb=1.0,
)
has_mi_min_manual_prob = self.cvDeterminer._will_have_mi(
joe_data,
outcome_model_repository=None,
vectorized=False,
manualMIProb=0.0,
)
self.assertTrue(has_mi_max_manual_prob)
self.assertFalse(has_mi_min_manual_prob)
def test_advance_outcomes_fatal_mi(self):
self._always_positive_repository.stroke_case_fatality = 1.0
self._always_positive_repository.mi_case_fatality = 1.0
self._always_positive_repository.manualStrokeMIProbability = 1.0
self.joe.advance_outcomes(self._always_positive_repository)
self.assertTrue(self.joe.has_mi_during_simulation())
self.assertFalse(self.joe.has_stroke_during_simulation())
self.assertTrue(self.joe.is_dead())
def test_advance_outcomes_fatal_stroke(self):
self._always_positive_repository.stroke_case_fatality = 1.0
self._always_positive_repository.mi_case_fatality = 1.0
self._always_positive_repository.manualStrokeMIProbability = 0.0
self.joe.advance_outcomes(self._always_positive_repository)
self.assertFalse(self.joe.has_mi_during_simulation())
self.assertTrue(self.joe.has_stroke_during_simulation())
self.assertTrue(self.joe.is_dead())
def test_advance_outcomes_nonfatal_mi(self):
self.assertEqual(0, self.joe.is_dead())
self._always_positive_repository.stroke_case_fatality = 0.0
self._always_positive_repository.mi_case_fatality = 0.0
self._always_positive_repository.manualStrokeMIProbability = 1.0
self.joe.advance_outcomes(self._always_positive_repository)
self.assertTrue(self.joe.has_mi_during_simulation())
self.assertFalse(self.joe.has_stroke_during_simulation())
def test_advance_outcomes_nonfatal_stroke(self):
self._always_positive_repository.stroke_case_fatality = 0.0
self._always_positive_repository.mi_case_fatality = 0.0
self._always_positive_repository.manualStrokeMIProbability = 0.0
self.joe.advance_outcomes(self._always_positive_repository)
self.assertFalse(self.joe.has_mi_during_simulation())
self.assertTrue(self.joe.has_stroke_during_simulation())
class TestGCPModel(unittest.TestCase):
def initializeAfib(person):
return None
def setUp(self):
self._test_case_one = Person(
age=65 - 0.828576318 * 10,
gender=NHANESGender.FEMALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=120 + 0.45 * 10,
dbp=80,
# guessingon the centering standard for glucose...may have to check
a1c=Person.convert_fasting_glucose_to_a1c(100 - 1.1 * 10),
hdl=50,
totChol=127 - 3.64 * 10,
ldl=90,
trig=150,
bmi=26.6 + 15.30517532,
waist=94 + 19.3,
anyPhysicalActivity=1,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.ONETOSIX,
antiHypertensiveCount=1,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=TestGCPModel.initializeAfib)
self._test_case_two = Person(
age=65 - 0.458555784 * 10,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=120 + 0.3 * 10,
dbp=80,
# guessingon the centering standard for glucose...may have to check
a1c=Person.convert_fasting_glucose_to_a1c(100 + 0.732746529 * 10),
hdl=50,
totChol=127 + 1.18 * 10,
ldl=90,
trig=150,
bmi=26.6 + 0.419305619,
waist=94 - 2.5,
anyPhysicalActivity=1,
education=Education.SOMEHIGHSCHOOL,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.ONETOSIX,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=TestGCPModel.initializeAfib)
self._test_case_three = Person(
age=65 - 0.358692676 * 10,
gender=NHANESGender.FEMALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_BLACK,
sbp=120 + 2.3 * 10,
dbp=80,
# guessingon the centering standard for glucose...may have to check
a1c=Person.convert_fasting_glucose_to_a1c(100 + 0.8893 * 10),
hdl=50,
totChol=127 + 4.7769 * 10,
ldl=90,
trig=150,
bmi=26.6 + 2.717159247,
waist=94 + 9,
anyPhysicalActivity=1,
education=Education.LESSTHANHIGHSCHOOL,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=1,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=TestGCPModel.initializeAfib)
def test_baseline_gcp(self):
# check that all of the random elements have been removed for testing...
self.assertEqual(
GCPModel().calc_linear_predictor(person=self._test_case_one,
test=True),
GCPModel().calc_linear_predictor(person=self._test_case_one,
test=True))
# this is for the first person in teh spreadsheet (1569nomas)...comparator is GCP + backing out the cohort effect (NOMAS)
self.assertAlmostEqual(64.45419405 - 2.7905,
GCPModel().calc_linear_predictor(
person=self._test_case_one, test=True),
places=1)
# this is for the 2nd person in the spreadhsheet (204180409594cardia)...comparabor is GCP margin + backing out the cohort effect (cardia)
self.assertAlmostEqual(50.01645213 + 1.3320,
GCPModel().calc_linear_predictor(
person=self._test_case_two, test=True),
places=1)
# this is for the the first black person in the spreadsheet (J150483aric)...comparator is GCP + no cohort effect (ARIC)
self.assertAlmostEqual(42.99471241,
GCPModel().calc_linear_predictor(
person=self._test_case_three, test=True),
places=1)
def test_gcp_after_one_year(self):
# this is for the first person in teh spreadsheet (1569nomas)...comparator is GCP + backing out the cohort effect (NOMAS)
self._test_case_one.advance_year(
DoNotChangeRiskFactorsModelRepository(),
AlwaysNegativeOutcomeRepository())
# account for the difference between the actual mean systolic change
self.assertAlmostEqual(64.28862964 - 2.7905,
GCPModel().calc_linear_predictor(
person=self._test_case_one, test=True),
places=1)
def test_gcp_random_effect(self):
self._test_case_one._randomEffects['gcp'] = 5
self.assertAlmostEqual(64.45419405 - 2.7905 + 5,
GCPModel().get_risk_for_person(
person=self._test_case_one,
years=1,
test=True),
places=1)
class TestPersonWaveStatus(unittest.TestCase):
def setUp(self):
self.oldJoe = Person(
age=60,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_BLACK,
sbp=140,
dbp=90,
a1c=5.5,
hdl=50,
totChol=200,
bmi=25,
ldl=90,
trig=150,
waist=45,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAFib)
self.youngJoe = Person(
age=40,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_BLACK,
sbp=140,
dbp=90,
a1c=5.5,
hdl=50,
totChol=200,
bmi=25,
ldl=90,
trig=150,
waist=45,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAFib)
def testStatusAfterFatalStroke(self):
self.youngJoe.advance_year(TestRiskModelRepository(),
AgeOver50CausesFatalStroke())
self.assertEqual(41, self.youngJoe._age[-1])
self.assertEqual(False, self.youngJoe.is_dead())
self.assertEqual(False, self.youngJoe._stroke)
self.assertEqual(True, self.youngJoe.alive_at_start_of_wave(1))
self.assertEqual(True, self.youngJoe.alive_at_start_of_wave(2))
with self.assertRaises(Exception):
self.youngJoe.alive_at_start_of_wave(3)
self.youngJoe.advance_year(TestRiskModelRepository(),
AgeOver50CausesFatalStroke())
self.assertEqual(True, self.youngJoe.alive_at_start_of_wave(3))
self.oldJoe.advance_year(TestRiskModelRepository(),
AgeOver50CausesFatalStroke())
self.assertEqual(60, self.oldJoe._age[-1])
self.assertEqual(True, self.oldJoe.is_dead())
self.assertEqual(True, self.oldJoe._stroke)
self.assertEqual(True, self.oldJoe.alive_at_start_of_wave(1))
self.assertEqual(False, self.oldJoe.alive_at_start_of_wave(2))
# this is called to verify that it DOES NOT throw an excepiotn
self.oldJoe.alive_at_start_of_wave(3)
def testNonCVMortalityLeadsToCorrectStatus(self):
self.youngJoe.advance_year(TestRiskModelRepository(),
AgeOver50CausesNonCVMortality())
self.assertEqual(41, self.youngJoe._age[-1])
self.assertEqual(False, self.youngJoe.is_dead())
self.assertEqual(False, self.youngJoe._stroke)
self.assertEqual(True, self.youngJoe.alive_at_start_of_wave(1))
self.assertEqual(True, self.youngJoe.alive_at_start_of_wave(2))
with self.assertRaises(Exception):
self.youngJoe.alive_at_start_of_wave(3)
self.youngJoe.advance_year(TestRiskModelRepository(),
AgeOver50CausesNonCVMortality())
self.assertEqual(True, self.youngJoe.alive_at_start_of_wave(3))
self.oldJoe.advance_year(TestRiskModelRepository(),
AgeOver50CausesNonCVMortality())
self.assertEqual(60, self.oldJoe._age[-1])
self.assertEqual(True, self.oldJoe.is_dead())
self.assertEqual(False, self.oldJoe._stroke)
self.assertEqual(True, self.oldJoe.alive_at_start_of_wave(1))
self.assertEqual(False, self.oldJoe.alive_at_start_of_wave(2))
# this is called to verify that it DOES NOT throw an excepiotn
self.oldJoe.alive_at_start_of_wave(3)
def testHasFatalStrokeInWaveIsCaptured(self):
self.oldJoe.advance_year(TestRiskModelRepository(),
AgeOver50CausesFatalStroke())
self.assertEqual(True, self.oldJoe.has_stroke_during_simulation())
self.assertEqual(True, self.oldJoe.has_stroke_during_wave(1))
with self.assertRaises(Exception):
self.oldJoe.has_stroke_during_wave(0)
# calling to make sure it does NOT raise an exception...you should
# be able to ask whether somebody has a stroke in a wave after they are dead, the answer is
# "No"
self.assertEqual(False, self.oldJoe.has_stroke_during_wave(2))
def testNonFatalStrokeInWaveWithNonCVDeathIsCaptured(self):
self.oldJoe.advance_year(TestRiskModelRepository(),
NonFatalStrokeAndNonCVMortality())
self.assertEqual(True, self.oldJoe.has_stroke_during_simulation())
self.assertEqual(True, self.oldJoe.has_stroke_during_wave(1))
self.assertEqual(True, self.oldJoe.is_dead())
class TestPersonAdvanceOutcomes(unittest.TestCase):
def setUp(self):
self.joe = Person(42, NHANESGender.MALE,
NHANESRaceEthnicity.NON_HISPANIC_BLACK, 140, 90, 5.5,
50, 200, 25, 90, 150, 45, 0,
Education.COLLEGEGRADUATE, SmokingStatus.NEVER,
AlcoholCategory.NONE, 0, 0, 0, initializeAFib)
self._always_positive_repository = AlwaysPositiveOutcomeRepository()
self._always_negative_repository = AlwaysNegativeOutcomeRepository()
self.cvDeterminer = CVOutcomeDetermination(
self._always_positive_repository)
def test_dead_is_dead_advance_year(self):
self.joe._alive[-1] = False
with self.assertRaises(RuntimeError,
msg="Person is dead. Can not advance year"):
self.joe.advance_year(None, None)
def test_dead_is_dead_advance_risk_factors(self):
self.joe._alive[-1] = False
with self.assertRaises(
RuntimeError,
msg="Person is dead. Can not advance risk factors"):
self.joe.advance_risk_factors(None)
def test_dead_is_dead_advance_outcomes(self):
self.joe._alive[-1] = False
with self.assertRaises(RuntimeError,
msg="Person is dead. Can not advance outcomes"):
self.joe.advance_outcomes(None)
def test_will_have_fatal_mi(self):
self.assertEqual(self.cvDeterminer._will_have_fatal_mi(self.joe, 1.0),
1)
self.assertEqual(self.cvDeterminer._will_have_fatal_mi(self.joe, 0.0),
0)
def test_fatal_mi_secondary_prob(self):
self.cvDeterminer.mi_secondary_case_fatality = 1.0
self.cvDeterminer.mi_case_fatality = 0.0
joeClone = copy.deepcopy(self.joe)
self.assertEqual(self.cvDeterminer._will_have_fatal_mi(joeClone, 0.0),
0)
joeClone._outcomes[OutcomeType.MI] = (joeClone._age,
Outcome(OutcomeType.MI, False))
# even though the passed fatality rate is zero, it shoudl be overriden by the
# secondary rate given that joeclone had a prior MI
self.assertEqual(self.cvDeterminer._will_have_fatal_mi(joeClone, 0.0),
1)
def test_fatal_stroke_secondary_prob(self):
self.cvDeterminer.stroke_secondary_case_fatality = 1.0
self.cvDeterminer.stroke_case_fatality = 0.0
joeClone = copy.deepcopy(self.joe)
self.assertEqual(
self.cvDeterminer._will_have_fatal_stroke(joeClone, 0.0), 0)
joeClone._outcomes[OutcomeType.STROKE] = (joeClone._age,
Outcome(
OutcomeType.STROKE,
False))
# even though the passed fatality rate is zero, it shoudl be overriden by the
# secondary rate given that joeclone had a prior stroke
self.assertEqual(
self.cvDeterminer._will_have_fatal_stroke(joeClone, 0.0), 1)
def test_will_have_fatal_stroke(self):
self.assertEqual(
self.cvDeterminer._will_have_fatal_stroke(self.joe, 1.0), 1)
self.assertEqual(
self.cvDeterminer._will_have_fatal_stroke(self.joe, 0.0), 0)
def test_has_mi_vs_stroke(self):
self.assertEqual(self.cvDeterminer._will_have_mi(self.joe, None, 1.0),
1)
self.assertEqual(self.cvDeterminer._will_have_mi(self.joe, None, 0.0),
0)
def test_advance_outcomes_fatal_mi(self):
self._always_positive_repository.stroke_case_fatality = 1.0
self._always_positive_repository.mi_case_fatality = 1.0
self._always_positive_repository.manualStrokeMIProbability = 1.0
self.joe.advance_outcomes(self._always_positive_repository)
self.assertTrue(self.joe.has_mi_during_simulation())
self.assertFalse(self.joe.has_stroke_during_simulation())
self.assertTrue(self.joe.is_dead())
def test_advance_outcomes_fatal_stroke(self):
self._always_positive_repository.stroke_case_fatality = 1.0
self._always_positive_repository.mi_case_fatality = 1.0
self._always_positive_repository.manualStrokeMIProbability = 0.0
self.joe.advance_outcomes(self._always_positive_repository)
self.assertFalse(self.joe.has_mi_during_simulation())
self.assertTrue(self.joe.has_stroke_during_simulation())
self.assertTrue(self.joe.is_dead())
def test_advance_outcomes_nonfatal_mi(self):
self.assertEqual(0, self.joe.is_dead())
self._always_positive_repository.stroke_case_fatality = 0.0
self._always_positive_repository.mi_case_fatality = 0.0
self._always_positive_repository.manualStrokeMIProbability = 1.0
self.joe.advance_outcomes(self._always_positive_repository)
self.assertTrue(self.joe.has_mi_during_simulation())
self.assertFalse(self.joe.has_stroke_during_simulation())
def test_advance_outcomes_nonfatal_stroke(self):
self._always_positive_repository.stroke_case_fatality = 0.0
self._always_positive_repository.mi_case_fatality = 0.0
self._always_positive_repository.manualStrokeMIProbability = 0.0
self.joe.advance_outcomes(self._always_positive_repository)
self.assertFalse(self.joe.has_mi_during_simulation())
self.assertTrue(self.joe.has_stroke_during_simulation())
def setUp(self):
# 2740200061fos
self._test_case_one = Person(
age=54.060233,
gender=NHANESGender.FEMALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=120,
dbp=80,
# guessingon the centering standard for glucose...may have to check
a1c=Person.convert_fasting_glucose_to_a1c(100),
hdl=50,
totChol=150,
ldl=90,
trig=150,
bmi=26.6,
waist=94,
anyPhysicalActivity=1,
education=Education.HIGHSCHOOLGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.ONETOSIX,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0,
initializeAfib=TestDementiaModel.initializeAfib,
initializationRepository=InitializationRepository(),
)
self._test_case_one._gcp[0] = 58.68
self._test_case_one._gcp.append(self._test_case_one._gcp[0] -
1.1078128)
# 2740201178fos
self._test_case_two = Person(
age=34.504449,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=120,
dbp=80,
# guessingon the centering standard for glucose...may have to check
a1c=Person.convert_fasting_glucose_to_a1c(100),
hdl=50,
totChol=150,
ldl=90,
trig=150,
bmi=26.6,
waist=94,
anyPhysicalActivity=1,
education=Education.SOMECOLLEGE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.ONETOSIX,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0,
initializeAfib=TestDementiaModel.initializeAfib,
initializationRepository=InitializationRepository(),
)
self._test_case_two._gcp[0] = 58.68
self._test_case_two._gcp.append(self._test_case_two._gcp[0] -
1.7339989)
self._test_case_one_parameteric = Person(
age=40,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_BLACK,
sbp=120,
dbp=80,
# guessingon the centering standard for glucose...may have to check
a1c=Person.convert_fasting_glucose_to_a1c(100),
hdl=50,
totChol=150,
ldl=90,
trig=150,
bmi=26.6,
waist=94,
anyPhysicalActivity=1,
education=Education.LESSTHANHIGHSCHOOL,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.ONETOSIX,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0,
initializeAfib=TestDementiaModel.initializeAfib,
initializationRepository=InitializationRepository(),
)
self._test_case_one_parameteric._gcp[0] = 25
# GCP slope is zero
self._test_case_one_parameteric._gcp.append(
self._test_case_one._gcp[0])
# test case 71 in rep_gdta.
self._test_case_two_parametric = Person(
age=80,
gender=NHANESGender.FEMALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_BLACK,
sbp=120,
dbp=80,
# guessingon the centering standard for glucose...may have to check
a1c=Person.convert_fasting_glucose_to_a1c(100),
hdl=50,
totChol=150,
ldl=90,
trig=150,
bmi=26.6,
waist=94,
anyPhysicalActivity=1,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.ONETOSIX,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0,
initializeAfib=TestDementiaModel.initializeAfib,
initializationRepository=InitializationRepository(),
)
self._test_case_two_parametric._gcp[0] = 75
self._test_case_two_parametric._gcp.append(self._test_case_two._gcp[0])
# test case 72 in rep_gdta.
self._test_case_three_parametric = Person(
age=80,
gender=NHANESGender.FEMALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=120,
dbp=80,
# guessingon the centering standard for glucose...may have to check
a1c=Person.convert_fasting_glucose_to_a1c(100),
hdl=50,
totChol=150,
ldl=90,
trig=150,
bmi=26.6,
waist=94,
anyPhysicalActivity=1,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.ONETOSIX,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0,
initializeAfib=TestDementiaModel.initializeAfib,
initializationRepository=InitializationRepository(),
)
self._test_case_three_parametric._gcp[0] = 75
self._test_case_three_parametric._gcp.append(
self._test_case_two._gcp[0])
self._population_dataframe = init_vectorized_population_dataframe([
self._test_case_one,
self._test_case_two,
])
class TestResetPersonAndRollBackEvents(unittest.TestCase):
def setUp(self):
self.baseAge = 55
self.baseSBP = 120
self._white_male = Person(
age=self.baseAge,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_copy_paste = Person(
age=self.baseAge,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_one_year = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_female = Person(
age=self.baseAge,
gender=NHANESGender.FEMALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._black_female = Person(
age=self.baseAge,
gender=NHANESGender.FEMALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_BLACK,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_sbp = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP + 10,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_dbp = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=90,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_a1c = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=7,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_hdl = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=60,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_totChol = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=223,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_ldl = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=100,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_trig = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=160,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_bmi = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=25,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_waist = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=36,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_activity = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=1,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_minus_edudcation = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=1,
education=Education.HIGHSCHOOLGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_smoking = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=1,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.CURRENT,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_bpMed = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=1,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_statin = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=1,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_lipid = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=1,
initializeAfib=initializeAfib)
self._white_male_plus_afib = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfibAlwaysPositive)
self._baseline_stroke_person = Person(
age=self.baseAge,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib,
selfReportStrokeAge=50)
self._baseline_stroke_person_copy_paste = Person(
age=self.baseAge,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib,
selfReportStrokeAge=50)
def testResetBasicAttributes(self):
self._white_male.advance_year(TestRiskModelRepository(),
AlwaysNonFatalStrokeOutcomeRepository())
self._baseline_stroke_person.advance_year(
TestRiskModelRepository(), AlwaysFatalStrokeOutcomeRepository())
self.assertEqual(2, len(self._white_male._dbp))
self._white_male.reset_to_baseline()
self.assertEqual(1, len(self._white_male._dbp))
self.assertEqual(1, len(self._white_male._totChol))
self.assertEqual(1, len(self._white_male._trig))
self.assertEqual(1, len(self._white_male._trig))
self.assertEqual(1, len(self._white_male._sbp))
self.assertEqual(self.baseSBP, self._white_male._sbp[-1])
self.assertEqual(self.baseAge, self._white_male._age[-1])
def testResetOutcomes(self):
self._white_male.advance_year(TestRiskModelRepository(),
AlwaysNonFatalStrokeOutcomeRepository())
self._baseline_stroke_person.advance_year(
TestRiskModelRepository(), AlwaysFatalStrokeOutcomeRepository())
self.assertEqual(1,
len(self._white_male._outcomes[OutcomeType.STROKE]))
self._white_male.reset_to_baseline()
self.assertEqual(0,
len(self._white_male._outcomes[OutcomeType.STROKE]))
def testResetOutcomesPreservesPreSimOutcomes(self):
self._white_male.advance_year(TestRiskModelRepository(),
AlwaysNonFatalStrokeOutcomeRepository())
self._baseline_stroke_person.advance_year(
TestRiskModelRepository(), AlwaysFatalStrokeOutcomeRepository())
self.assertEqual(
2, len(self._baseline_stroke_person._outcomes[OutcomeType.STROKE]))
self._baseline_stroke_person.reset_to_baseline()
self.assertEqual(
1, len(self._baseline_stroke_person._outcomes[OutcomeType.STROKE]))
def testRollbackNonFatalEvent(self):
self._white_male.advance_year(TestRiskModelRepository(),
AlwaysNonFatalStrokeOutcomeRepository())
self._baseline_stroke_person.advance_year(
TestRiskModelRepository(), AlwaysFatalStrokeOutcomeRepository())
self.assertEqual(False,
self._white_male.has_stroke_prior_to_simulation())
self.assertEqual(True, self._white_male.has_stroke_during_simulation())
self.assertEqual(True, self._white_male.has_stroke_during_wave(1))
self._white_male.rollback_most_recent_event(OutcomeType.STROKE)
self.assertEqual(False,
self._white_male.has_stroke_prior_to_simulation())
self.assertEqual(False, self._white_male.has_stroke_during_wave(1))
self.assertEqual(False,
self._white_male.has_stroke_during_simulation())
def testRollbackFatalEvent(self):
self._white_male.advance_year(TestRiskModelRepository(),
AlwaysNonFatalStrokeOutcomeRepository())
self._baseline_stroke_person.advance_year(
TestRiskModelRepository(), AlwaysFatalStrokeOutcomeRepository())
self.assertEqual(
True, self._baseline_stroke_person.has_stroke_during_simulation())
self.assertEqual(
True,
self._baseline_stroke_person.has_stroke_prior_to_simulation())
self.assertEqual(
True, self._baseline_stroke_person.has_stroke_during_wave(1))
self.assertEqual(True, self._baseline_stroke_person.is_dead())
self._baseline_stroke_person.rollback_most_recent_event(
OutcomeType.STROKE)
self.assertEqual(
True,
self._baseline_stroke_person.has_stroke_prior_to_simulation())
self.assertEqual(
False, self._baseline_stroke_person.has_stroke_during_wave(1))
self.assertEqual(
False, self._baseline_stroke_person.has_stroke_during_simulation())
self.assertEqual(False, self._baseline_stroke_person.is_dead())
self.assertEqual(self.baseAge + 1,
self._baseline_stroke_person._age[-1])
def testBasePatientEquals(self):
self.assertTrue(self._white_male == self._white_male_copy_paste)
self.assertEqual(self._white_male, self._white_male_copy_paste)
self.assertNotEqual(self._white_male, self._white_female)
self.assertNotEqual(self._white_female, self._black_female)
self.assertNotEqual(self._white_male,
self._white_male_minus_edudcation)
self.assertNotEqual(self._white_male, self._white_male_plus_a1c)
self.assertNotEqual(self._white_male, self._white_male_plus_activity)
self.assertNotEqual(self._white_male, self._white_male_plus_afib)
self.assertNotEqual(self._white_male, self._white_male_plus_bmi)
self.assertNotEqual(self._white_male, self._white_male_plus_dbp)
self.assertNotEqual(self._white_male, self._white_male_plus_hdl)
self.assertNotEqual(self._white_male, self._white_male_plus_ldl)
self.assertNotEqual(self._white_male, self._white_male_plus_lipid)
self.assertNotEqual(self._white_male, self._white_male_plus_one_year)
self.assertNotEqual(self._white_male, self._white_male_plus_sbp)
self.assertNotEqual(self._white_male, self._white_male_plus_smoking)
self.assertNotEqual(self._white_male, self._white_male_plus_statin)
self.assertNotEqual(self._white_male, self._white_male_plus_totChol)
self.assertNotEqual(self._white_male, self._white_male_plus_trig)
self.assertNotEqual(self._white_male, self._white_male_plus_waist)
def testBaselineEqualityAfterAdvancingAYear(self):
self._white_male.advance_year(TestRiskModelRepository(),
NothingHappensRepository())
self._white_male_copy_paste.advance_year(TestRiskModelRepository(),
NothingHappensRepository())
self.assertEqual(self._white_male, self._white_male_copy_paste)
def testBasePatientWithBaselineStroke(self):
self.assertNotEqual(self._white_male, self._baseline_stroke_person)
self.assertEqual(self._baseline_stroke_person,
self._baseline_stroke_person_copy_paste)
self._baseline_stroke_person.advance_year(TestRiskModelRepository(),
NothingHappensRepository())
self._baseline_stroke_person_copy_paste.advance_year(
TestRiskModelRepository(), NothingHappensRepository())
self.assertEqual(self._baseline_stroke_person,
self._baseline_stroke_person_copy_paste)
def testBasePatientWithCVEvents(self):
self.assertEqual(self._white_male_copy_paste, self._white_male)
self._white_male.advance_year(TestRiskModelRepository(),
AlwaysNonFatalStrokeOutcomeRepository())
self.assertNotEqual(self._white_male, self._white_male_copy_paste)
self._white_male_copy_paste.advance_year(
TestRiskModelRepository(), AlwaysNonFatalStrokeOutcomeRepository())
self.assertEqual(self._white_male_copy_paste, self._white_male)
def testBasePatientWithNonCVDeath(self):
self.assertEqual(self._white_male_copy_paste, self._white_male)
self._white_male.advance_year(TestRiskModelRepository(),
AlwaysNonCVDeathRepository())
self.assertNotEqual(self._white_male, self._white_male_copy_paste)
self._white_male_copy_paste.advance_year(TestRiskModelRepository(),
NothingHappensRepository())
self.assertNotEqual(self._white_male_copy_paste, self._white_male)
def testBasePatientWithNonCVDeathOtherWay(self):
self.assertEqual(self._white_male_copy_paste, self._white_male)
self._white_male.advance_year(TestRiskModelRepository(),
AlwaysNonCVDeathRepository())
self.assertNotEqual(self._white_male, self._white_male_copy_paste)
self._white_male_copy_paste.advance_year(TestRiskModelRepository(),
AlwaysNonCVDeathRepository())
self.assertEqual(self._white_male_copy_paste, self._white_male)
def testDeepCopy(self):
self.assertEqual(self._white_male, copy.deepcopy(self._white_male))
self._white_male.advance_year(TestRiskModelRepository(),
NothingHappensRepository())
self.assertEqual(self._white_male, copy.deepcopy(self._white_male))
self._white_male.advance_year(TestRiskModelRepository(),
AlwaysNonFatalStrokeOutcomeRepository())
self.assertEqual(self._white_male, copy.deepcopy(self._white_male))
self._white_male.advance_year(TestRiskModelRepository(),
AlwaysNonCVDeathRepository())
self.assertEqual(self._white_male, copy.deepcopy(self._white_male))
def setUp(self):
self._white_male = Person(
age=55,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=120,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._black_male = Person(
age=55,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_BLACK,
sbp=120,
dbp=80,
a1c=6,
hdl=50,
totChol=200,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._black_treated_male = Person(
age=55,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_BLACK,
sbp=120,
dbp=80,
a1c=6,
hdl=50,
totChol=200,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=1,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_female = Person(
age=55,
gender=NHANESGender.FEMALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=120,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._black_female = Person(
age=55,
gender=NHANESGender.FEMALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_BLACK,
sbp=120,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._outcome_model_repository = OutcomeModelRepository()
def setUp(self):
self.baseAge = 55
self.baseSBP = 120
self._white_male = Person(
age=self.baseAge,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_copy_paste = Person(
age=self.baseAge,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_one_year = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_female = Person(
age=self.baseAge,
gender=NHANESGender.FEMALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._black_female = Person(
age=self.baseAge,
gender=NHANESGender.FEMALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_BLACK,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_sbp = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP + 10,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_dbp = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=90,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_a1c = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=7,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_hdl = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=60,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_totChol = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=223,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_ldl = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=100,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_trig = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=160,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_bmi = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=25,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_waist = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=36,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_activity = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=1,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_minus_edudcation = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=1,
education=Education.HIGHSCHOOLGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_smoking = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=1,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.CURRENT,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_bpMed = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=1,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_statin = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=1,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib)
self._white_male_plus_lipid = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=1,
initializeAfib=initializeAfib)
self._white_male_plus_afib = Person(
age=self.baseAge + 1,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfibAlwaysPositive)
self._baseline_stroke_person = Person(
age=self.baseAge,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib,
selfReportStrokeAge=50)
self._baseline_stroke_person_copy_paste = Person(
age=self.baseAge,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=self.baseSBP,
dbp=80,
a1c=6,
hdl=50,
totChol=213,
ldl=90,
trig=150,
bmi=22,
waist=34,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=initializeAfib,
selfReportStrokeAge=50)
class TestTreatmentStrategy(unittest.TestCase):
def initializeAfib(person):
return None
def setUp(self):
self.baselineSBP = 140
self.baselineDBP = 80
self._test_person = Person(
age=75,
gender=0,
raceEthnicity=1,
sbp=self.baselineSBP,
dbp=self.baselineDBP,
a1c=6.5,
hdl=50,
totChol=210,
ldl=90,
trig=150,
bmi=22,
waist=50,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=1,
alcohol=0,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
initializeAfib=TestTreatmentStrategy.initializeAfib)
self._risk_model_repository = TestRiskModelRepository()
# setup so that the SBP always stays the same
self._risk_model_repository._repository['sbp']._params['age'] = 0
self._risk_model_repository._repository['sbp']._params['sbp'] = 1.0
self._risk_model_repository._repository['sbp']._params['intercept'] = 0
self._risk_model_repository._repository['dbp']._params['age'] = 0
self._risk_model_repository._repository['dbp']._params['dbp'] = 1.0
self._risk_model_repository._repository['dbp']._params['sbp'] = 0
self._risk_model_repository._repository['dbp']._params['intercept'] = 0
# setup so that the anti-hypertensive count stays at zero
self._risk_model_repository._repository[
'antiHypertensiveCount']._params['age'] = 0
self._risk_model_repository._repository[
'antiHypertensiveCount']._params['sbp'] = 0
self._risk_model_repository._repository[
'antiHypertensiveCount']._params['intercept'] = 0
def add_a_single_blood_pressure_medication_strategy(person):
return {'_antiHypertensiveCount': 1}, {'_sbp': -5, '_dbp': -3}, {}
def testSimpleBPTreatmentStrategy(self):
self._test_person.advance_treatment(self._risk_model_repository)
self._test_person.advance_risk_factors(self._risk_model_repository)
self.assertEqual(self.baselineSBP, self._test_person._sbp[1])
self.assertEqual(self.baselineDBP, self._test_person._dbp[1])
self.assertEqual(0, self._test_person._antiHypertensiveCount[1])
self._test_person._bpTreatmentStrategy = TestTreatmentStrategy.add_a_single_blood_pressure_medication_strategy
self._test_person.advance_treatment(self._risk_model_repository)
self._test_person.advance_risk_factors(self._risk_model_repository)
self.assertEqual(self.baselineSBP - 5, self._test_person._sbp[2])
self.assertEqual(self.baselineDBP - 3, self._test_person._dbp[2])
self.assertEqual(1, self._test_person._antiHypertensiveCount[2])
def setUp(self):
popSize = 100
age = np.random.normal(loc=70, scale=20, size=popSize)
sbp = age * 1.05 + np.random.normal(loc=40, scale=30, size=popSize)
df = pd.DataFrame({"age": age, "sbp": sbp})
simpleModel = statsmodel.ols(formula="sbp ~ age", data=df)
self.simpleModelResultSM = simpleModel.fit()
self.simpleModelResult = RegressionModel(
self.simpleModelResultSM.params.to_dict(),
self.simpleModelResultSM.bse.to_dict(),
self.simpleModelResultSM.resid.mean(),
self.simpleModelResultSM.resid.std(),
)
self.person = Person(
age=80,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=120,
dbp=80,
a1c=5.5,
hdl=50,
totChol=200,
bmi=27,
ldl=90,
trig=150,
waist=70,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0.0,
initializeAfib=initializeAfib,
)
self.people = [
Person(
age=80,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=bpinstance,
dbp=80,
a1c=5.5,
hdl=50,
totChol=200,
bmi=27,
ldl=90,
trig=150,
waist=70,
anyPhysicalActivity=0,
education=Education.COLLEGEGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.NONE,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0.0,
initializeAfib=initializeAfib,
) for bpinstance in sbp
]
for person in self.people:
self.advancePerson(person)
self.population_dataframe = init_vectorized_population_dataframe(
self.people,
with_base_gcp=True,
)
df2 = pd.DataFrame({
"age":
age,
"sbp": [person._sbp[-1] for person in self.people],
"meanSbp":
[np.array(person._sbp).mean() for person in self.people],
})
self.meanModelResultSM = statsmodel.ols(formula="sbp ~ age + meanSbp",
data=df2).fit()
self.meanModelResult = RegressionModel(
self.meanModelResultSM.params.to_dict(),
self.meanModelResultSM.bse.to_dict(),
self.meanModelResultSM.resid.mean(),
self.meanModelResultSM.resid.std(),
)
df3 = pd.DataFrame({
"age":
age,
"sbp": [person._sbp[-1] for person in self.people],
"logMeanSbp":
[np.log(np.array(person._sbp).mean()) for person in self.people],
})
self.logMeanModelResultSM = statsmodel.ols(
formula="sbp ~ age + logMeanSbp", data=df3).fit()
self.logMeanModelResult = RegressionModel(
self.logMeanModelResultSM.params.to_dict(),
self.logMeanModelResultSM.bse.to_dict(),
self.logMeanModelResultSM.resid.mean(),
self.logMeanModelResultSM.resid.std(),
)
race = np.random.randint(1, 5, size=popSize)
df4 = pd.DataFrame({"age": age, "sbp": sbp, "raceEthnicity": race})
df4.raceEthnicity = df4.raceEthnicity.astype("category")
self.raceModelResultSM = statsmodel.ols(
formula="sbp ~ age + raceEthnicity", data=df4).fit()
self.raceModelResult = RegressionModel(
self.raceModelResultSM.params.to_dict(),
self.raceModelResultSM.bse.to_dict(),
self.raceModelResultSM.resid.mean(),
self.raceModelResultSM.resid.std(),
)
dfMeanAndLag = pd.DataFrame({
"age":
age,
"sbp": [person._sbp[-1] for person in self.people],
"meanSbp":
[np.array(person._sbp).mean() for person in self.people],
"lagSbp": [person._sbp[-1] for person in self.people],
})
self.meanLagModelResultSM = statsmodel.ols(
formula="sbp ~ age + meanSbp + lagSbp", data=dfMeanAndLag).fit()
self.meanLagModelResult = RegressionModel(
self.meanLagModelResultSM.params.to_dict(),
self.meanLagModelResultSM.bse.to_dict(),
self.meanLagModelResultSM.resid.mean(),
self.meanLagModelResultSM.resid.std(),
)
self.ageSbpInteractionCoeff = 0.02
self.sbpInteractionCoeff = 0.5
self.interactionModel = RegressionModel(
{
"meanSbp#age": self.ageSbpInteractionCoeff,
"meanSbp": self.sbpInteractionCoeff,
"Intercept": 0,
},
{
"meanSbp#age": 0.0,
"meanSbp": 0.0
},
0,
0,
)
def setUp(self):
self._black_female_high_cr = Person(
age=52,
gender=NHANESGender.FEMALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_BLACK,
sbp=120,
dbp=80,
a1c=Person.convert_fasting_glucose_to_a1c(100),
hdl=50,
totChol=150,
ldl=90,
trig=150,
bmi=26.6,
waist=94,
anyPhysicalActivity=1,
education=Education.HIGHSCHOOLGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.ONETOSIX,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0.8,
initializeAfib=TestCKDEquation.initializeAfib,
)
self._black_female_low_cr = Person(
age=52,
gender=NHANESGender.FEMALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_BLACK,
sbp=120,
dbp=80,
a1c=Person.convert_fasting_glucose_to_a1c(100),
hdl=50,
totChol=150,
ldl=90,
trig=150,
bmi=26.6,
waist=94,
anyPhysicalActivity=1,
education=Education.HIGHSCHOOLGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.ONETOSIX,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0.4,
initializeAfib=TestCKDEquation.initializeAfib,
)
self._white_male_high_cr = Person(
age=52,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=120,
dbp=80,
a1c=Person.convert_fasting_glucose_to_a1c(100),
hdl=50,
totChol=150,
ldl=90,
trig=150,
bmi=26.6,
waist=94,
anyPhysicalActivity=1,
education=Education.HIGHSCHOOLGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.ONETOSIX,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=1.2,
initializeAfib=TestCKDEquation.initializeAfib,
)
self._white_male_low_cr = Person(
age=52,
gender=NHANESGender.MALE,
raceEthnicity=NHANESRaceEthnicity.NON_HISPANIC_WHITE,
sbp=120,
dbp=80,
a1c=Person.convert_fasting_glucose_to_a1c(100),
hdl=50,
totChol=150,
ldl=90,
trig=150,
bmi=26.6,
waist=94,
anyPhysicalActivity=1,
education=Education.HIGHSCHOOLGRADUATE,
smokingStatus=SmokingStatus.NEVER,
alcohol=AlcoholCategory.ONETOSIX,
antiHypertensiveCount=0,
statin=0,
otherLipidLoweringMedicationCount=0,
creatinine=0.1,
initializeAfib=TestCKDEquation.initializeAfib,
)