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)