zepid.base.Sensitivity¶
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class
zepid.base.
Sensitivity
(alpha=0.05)¶ Generates the sensitivity and (1-alpha)% confidence interval, comparing test results to disease status from pandas dataframe
Sensitivity is calculated from
\[Sensitivity = \frac{TP}{P}\]Wald standard error is
\[SE_{Wald} = \left(\frac{1}{TP} - \frac{1}{P}\right)^{\frac{1}{2}}\]Note
Disease & Test must be coded as (1: yes, 0:no)
Parameters: alpha (float, optional) – Alpha value to calculate two-sided Wald confidence intervals. Default is 95% confidence interval Examples
Calculate the sensitivity in a data set
>>> from zepid import Sensitivity, load_sample_data >>> df = load_sample_data(False) >>> sens = Sensitivity() >>> sens.fit(df, test='art', disease='dead') # Note this example is not great... ART is a treatment not test >>> sens.summary()
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__init__
(alpha=0.05)¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([alpha])Initialize self. fit
(df, test, disease)Calculates the Sensitivity summary
([decimal])Prints the summary results -