97 lines
3.6 KiB
Python
97 lines
3.6 KiB
Python
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import pytest
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import numpy as np
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from numpy.testing import assert_allclose, assert_equal
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from scipy.stats.contingency import relative_risk
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# Test just the calculation of the relative risk, including edge
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# cases that result in a relative risk of 0, inf or nan.
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@pytest.mark.parametrize(
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'exposed_cases, exposed_total, control_cases, control_total, expected_rr',
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[(1, 4, 3, 8, 0.25 / 0.375),
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(0, 10, 5, 20, 0),
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(0, 10, 0, 20, np.nan),
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(5, 15, 0, 20, np.inf)]
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)
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def test_relative_risk(exposed_cases, exposed_total,
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control_cases, control_total, expected_rr):
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result = relative_risk(exposed_cases, exposed_total,
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control_cases, control_total)
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assert_allclose(result.relative_risk, expected_rr, rtol=1e-13)
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def test_relative_risk_confidence_interval():
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result = relative_risk(exposed_cases=16, exposed_total=128,
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control_cases=24, control_total=256)
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rr = result.relative_risk
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ci = result.confidence_interval(confidence_level=0.95)
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# The corresponding calculation in R using the epitools package.
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#
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# > library(epitools)
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# > c <- matrix(c(232, 112, 24, 16), nrow=2)
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# > result <- riskratio(c)
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# > result$measure
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# risk ratio with 95% C.I.
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# Predictor estimate lower upper
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# Exposed1 1.000000 NA NA
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# Exposed2 1.333333 0.7347317 2.419628
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#
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# The last line is the result that we want.
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assert_allclose(rr, 4/3)
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assert_allclose((ci.low, ci.high), (0.7347317, 2.419628), rtol=5e-7)
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def test_relative_risk_ci_conflevel0():
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result = relative_risk(exposed_cases=4, exposed_total=12,
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control_cases=5, control_total=30)
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rr = result.relative_risk
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assert_allclose(rr, 2.0, rtol=1e-14)
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ci = result.confidence_interval(0)
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assert_allclose((ci.low, ci.high), (2.0, 2.0), rtol=1e-12)
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def test_relative_risk_ci_conflevel1():
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result = relative_risk(exposed_cases=4, exposed_total=12,
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control_cases=5, control_total=30)
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ci = result.confidence_interval(1)
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assert_equal((ci.low, ci.high), (0, np.inf))
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def test_relative_risk_ci_edge_cases_00():
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result = relative_risk(exposed_cases=0, exposed_total=12,
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control_cases=0, control_total=30)
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assert_equal(result.relative_risk, np.nan)
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ci = result.confidence_interval()
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assert_equal((ci.low, ci.high), (np.nan, np.nan))
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def test_relative_risk_ci_edge_cases_01():
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result = relative_risk(exposed_cases=0, exposed_total=12,
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control_cases=1, control_total=30)
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assert_equal(result.relative_risk, 0)
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ci = result.confidence_interval()
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assert_equal((ci.low, ci.high), (0.0, np.nan))
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def test_relative_risk_ci_edge_cases_10():
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result = relative_risk(exposed_cases=1, exposed_total=12,
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control_cases=0, control_total=30)
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assert_equal(result.relative_risk, np.inf)
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ci = result.confidence_interval()
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assert_equal((ci.low, ci.high), (np.nan, np.inf))
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@pytest.mark.parametrize('ec, et, cc, ct', [(0, 0, 10, 20),
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(-1, 10, 1, 5),
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(1, 10, 0, 0),
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(1, 10, -1, 4)])
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def test_relative_risk_bad_value(ec, et, cc, ct):
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with pytest.raises(ValueError, match="must be an integer not less than"):
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relative_risk(ec, et, cc, ct)
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def test_relative_risk_bad_type():
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with pytest.raises(TypeError, match="must be an integer"):
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relative_risk(1, 10, 2.0, 40)
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