zepid.calc.utils.risk_ci¶
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zepid.calc.utils.risk_ci(events, total, alpha=0.05, confint='wald')¶ Calculate two-sided risk confidence intervals
Risk is calculated from
\[R = \frac{a}{a+b}\]Wald standard error is
\[SE_{Wald} = \left(\frac{1}{a} - \frac{1}{b}\right)^{\frac{1}{2}}\]Hypergeometric standard error is
\[SE_{HypGeo} = \left(\frac{a b}{(a+b)^2 (a+b-1)}\right)^{\frac{1}{2}}\]Parameters: - events (integer, float) – Number of events/outcomes that occurred
- total (integer, float) – Total number of subjects that could have experienced the event
- alpha (float, optional) – Alpha level. Default is 0.05
- confint (string, optional) – Type of confidence interval to generate. Current options include Wald or Hypergeometric confidence intervals
Returns: Tuple containing risk, lower CL, upper CL, SE
Return type: tuple
Note
Confidence intervals rely on the central limit theorem, so there must be at least 5 events and 5 nonevents
Examples
Estimate the risk, standard error, and confidence intervals
>>> from zepid.calc import risk_ci >>> r = risk_ci(45, 100)
Extracting the estimated risk
>>> r.point_estimate
Extracting the lower and upper confidence intervals, respectively
>>> r.lower_bound >>> r.upper_bound
Extracting the standard error
>>> r.standard_error