zepid.base.IncidenceRateRatio

class zepid.base.IncidenceRateRatio(reference=0, alpha=0.05)

Estimates of Incidence Rate Ratio with a (1-alpha)*100% Confidence interval. Missing data is ignored

Incidence rate ratio is calculated from

\[IR = \frac{a}{t_1} / \frac{c}{t_0}\]

Incidence rate ratio standard error is

\[SE = \left(\frac{1}{a} + \frac{1}{c}\right)^{\frac{1}{2}}\]

Note

Outcome must be coded as (1: yes, 0:no). Only works for binary outcomes

Parameters:

• reference (integer, optional) – Reference category for comparisons. Default reference category is 0
• alpha (float, optional) – Alpha value to calculate two-sided Wald confidence intervals. Default is 95% confidence interval

Examples

Calculate the incidence rate ratio in a data set

>>> from zepid import IncidenceRateRatio, load_sample_data
>>> df = load_sample_data(False)
>>> irr = IncidenceRateRatio()
>>> irr.fit(df, exposure='art', outcome='dead', time='t')
>>> irr.summary()

Calculate the incidence rate ratio with exposure of ‘1’ as the reference category

>>> irr = IncidenceRateRatio(reference=1)
>>> irr.fit(df, exposure='art', outcome='dead', time='t')
>>> irr.summary()

Generate a plot of the calculated incidence rate ratio(s)

>>> import matplotlib.pyplot as plt
>>> irr = IncidenceRateRatio()
>>> irr.fit(df, exposure='art', outcome='dead', time='t')
>>> irr.plot()
>>> plt.show()

__init__(reference=0, alpha=0.05)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([reference, alpha])	Initialize self.
fit(df, exposure, outcome, time)	Calculate the Incidence Rate Ratio
plot([measure, scale, center])	Plot the risk ratios or the risks along with their corresponding confidence intervals.
summary([decimal])	Prints the summary results