scipy.stats._result_classes.RelativeRiskResult.confidence_interval#

RelativeRiskResult.confidence_interval(confidence_level=0.95)[source]#

Compute the confidence interval for the relative risk.

The confidence interval is computed using the Katz method (i.e. “Method C” of [1]; see also [2], section 3.1.2).

Parameters

confidence_levelfloat, optional: The confidence level to use for the confidence interval. Default is 0.95.

Returns

ciConfidenceInterval instance: The return value is an object with attributes low and high that hold the confidence interval.

References

1: D. Katz, J. Baptista, S. P. Azen and M. C. Pike, “Obtaining confidence intervals for the risk ratio in cohort studies”, Biometrics, 34, 469-474 (1978).
2: Hardeo Sahai and Anwer Khurshid, Statistics in Epidemiology, CRC Press LLC, Boca Raton, FL, USA (1996).

Examples

>>> from scipy.stats.contingency import relative_risk
>>> result = relative_risk(exposed_cases=10, exposed_total=75,
...                        control_cases=12, control_total=225)
>>> result.relative_risk
2.5
>>> result.confidence_interval()
ConfidenceInterval(low=1.1261564003469628, high=5.549850800541033)