Endometrial cancer (EC) - screening

The built-in process EC_screening simulates screening for endometrial cancer. Any Universe with this process should also include an instance of the EC process. The code in the EC screening process is meant for illustration purposes only and should not be used to inform decision analyses.

ec_screening = processes.EC_screening(
    strategy=processes.ec_screening.data.example.strategy,
    sensitivity=processes.ec_screening.data.example.sensitivity,
    p_hysterectomy=processes.ec_screening.data.example.p_hysterectomy,
    max_rounds=len(processes.ec_screening.data.example.strategy),
    name="ec_screening"
)

Note

The parameter max_rounds is used in the properties() method of EC_screening. Therefore, this parameter may not be different across Universes.

Strategy

The strategy parameter lists the screening interventions. It requires a sequence of dictionaries with the ages of screening and participation rate for each screening age. The following keys should be listed:

• age: age of the screening test;

• participation_first: probability to attend screening at the first screening invitation;

• participation_participator: probability to attend screening if someone participated in screening in the previous round;

• participation_non_participator: probability to attend screening if someone did not participate in screening in the previous round.

strategy = [
    {
        "age": age,
        "participation_first": 1.0,
        "participation_participator": 1.0,
        "participation_non_participator": 1.0,
    }
    for age in range(30, 36, 1)
]

Sensitivity

The sensitivity parameter lists the sensitivity of a test to the different types of lesions. It should list the sensitivity to:

• Hyperplasia with atypia;

• Hyperplasia without atypia;

• Preclinical cancers.

sensitivity = {
    "hyperplasia_with_atypia": 0.56,
    "hyperplasia_without_atypia": 0.56,
    "preclinical": 0.96,
}

Probability of hysterectomy

The parameter p_hysterectomy specifies the probability that the woman will undergo hysterectomy once a certain lesion has been detected. Probabilities should be specified for hyperplasia with and without atypia, and for individuals with a negative test. Note that the probability for a negative test can be considered the specificity of the test.

p_hysterectomy = {
    "negative": 0.00001,
    "hyperplasia_with_atypia": 0.05,
    "hyperplasia_without_atypia": 0.05,
}

Maximum number of screening rounds

For all individuals, the EC_screening Process draws random numbers for each screening round in the properties() to handle test sensitivity, participation and the probability of hysterectomy. Therefore, the Process needs to know how many screening tests are scheduled at most over all Universes. This should be specified in the max_rounds parameter. For example, if strategies is a list containing all strategies that are evaluated in the simulation, max_rounds could be specified as follows:

max_rounds = max([len(strategy) for strategy in strategies])

If only one screening strategy is evaluated or all strategies have an equal number of screening rounds, this parameter can be left unspecified.

Example code

ec_screening.py

from miscore import Model, processes, Universe

# Birth process
birth = processes.Birth(
    name="birth",
    year=1938
)

# OC process
oc = processes.OC(
    name="oc",
    life_table=processes.oc.data.us_2017.life_table_female
)

# EC process
ec = processes.EC.from_data(
    processes.ec.data.us,
    onset_method="before_oc"
)

ec_screening = processes.EC_screening(
    strategy=processes.ec_screening.data.example.strategy,
    sensitivity=processes.ec_screening.data.example.sensitivity,
    p_hysterectomy=processes.ec_screening.data.example.p_hysterectomy,
    max_rounds=len(processes.ec_screening.data.example.strategy),
    name="ec_screening"
)

# Universes
no_screening = Universe(
    name="no_screening",
    processes=[
        birth,
        oc,
        ec
    ]
)
screening = Universe(
    name="screening",
    processes=[
        birth,
        oc,
        ec,
        ec_screening
    ]
)

# Model
model = Model(
    universes=[
        no_screening,
        screening
    ]
)

# Run model
result = model.run(
    n=1e5,
    seeds_properties={
        "birth": 1646,
        "oc": 9321,
        "ec": 4841
    },
    seeds_properties_tmp={
        "ec": 8010
    },
    event_ages=range(0, 101, 1),
    duration_ages=range(0, 101, 1),
    verbose=True
)

# Export
result.events.to_csv("events.csv")
result.durations.to_csv("durations.csv")

pivot_events = result.events.pivot_table(
    values="number", index="age", columns=["universe", "tag"]
)
pivot_durations = result.durations.pivot_table(
    values="number", index="age", columns=["universe", "tag"]
)
pivot_events.to_csv('pivot_events.csv')
pivot_durations.to_csv('pivot_durations.csv')