def test_relative_risk_ci_conflevel1():
result = relative_risk(exposed_cases=4,
exposed_total=12,
control_cases=5,
control_total=30)
ci = result.confidence_interval(1)
assert_equal((ci.low, ci.high), (0, np.inf))
def test_relative_risk_ci_edge_cases_10():
result = relative_risk(exposed_cases=1,
exposed_total=12,
control_cases=0,
control_total=30)
assert_equal(result.relative_risk, np.inf)
ci = result.confidence_interval()
assert_equal((ci.low, ci.high), (np.nan, np.inf))
def test_relative_risk_ci_conflevel0():
result = relative_risk(exposed_cases=4,
exposed_total=12,
control_cases=5,
control_total=30)
rr = result.relative_risk
assert_allclose(rr, 2.0, rtol=1e-14)
ci = result.confidence_interval(0)
assert_allclose((ci.low, ci.high), (2.0, 2.0), rtol=1e-12)
def test_relative_risk_confidence_interval():
result = relative_risk(exposed_cases=16,
exposed_total=128,
control_cases=24,
control_total=256)
rr = result.relative_risk
ci = result.confidence_interval(confidence_level=0.95)
# The corresponding calculation in R using the epitools package.
#
# > library(epitools)
# > c <- matrix(c(232, 112, 24, 16), nrow=2)
# > result <- riskratio(c)
# > result$measure
# risk ratio with 95% C.I.
# Predictor estimate lower upper
# Exposed1 1.000000 NA NA
# Exposed2 1.333333 0.7347317 2.419628
#
# The last line is the result that we want.
assert_allclose(rr, 4 / 3)
assert_allclose((ci.low, ci.high), (0.7347317, 2.419628), rtol=5e-7)
def test_relative_risk_bad_type():
with pytest.raises(TypeError, match="must be an integer"):
relative_risk(1, 10, 2.0, 40)
def test_relative_risk_bad_value(ec, et, cc, ct):
with pytest.raises(ValueError, match="must be an integer not less than"):
relative_risk(ec, et, cc, ct)
def test_relative_risk(exposed_cases, exposed_total, control_cases,
control_total, expected_rr):
result = relative_risk(exposed_cases, exposed_total, control_cases,
control_total)
assert_allclose(result.relative_risk, expected_rr, rtol=1e-13)