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._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_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,
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.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.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 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.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 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
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):
# 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,
])
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,
)