zepid.base.IncidenceRateDifference

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

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

Incidence rate difference is calculated from

\[ID = \frac{a}{t_1} - \frac{c}{t_0}\]

Incidence rate difference standard error is

\[SE = \left(\frac{a}{t_1^2} + \frac{c}{t_0^2}\right)^{\frac{1}{2}}\]

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 difference in a data set

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

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

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

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

>>> import matplotlib.pyplot as plt
>>> ird = IncidenceRateDifference()
>>> ird.fit(df, exposure='art', outcome='dead', time='t')
>>> ird.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)	Calculates the Incidence Rate Difference
plot([measure, center])	Plot the incidence rate differences or the incidence rates along with their corresponding confidence intervals.
summary([decimal])	Prints the summary results