statsmodels.stats.rates.power_equivalence_poisson_2indep

statsmodels.stats.rates.power_equivalence_poisson_2indep(rate1, rate2, nobs1, low, upp, nobs_ratio=1, exposure=1, alpha=0.05, dispersion=1, method_var='alt', return_results=False)

Power of equivalence test of ratio of 2 independent poisson rates

Parameters:

rate1float

Poisson rate for the first sample, treatment group, under the alternative hypothesis.

rate2float

Poisson rate for the second sample, reference group, under the alternative hypothesis.

nobs1float or int

Number of observations in sample 1.

lowfloat

Lower equivalence margin for the rate ratio, rate1 / rate2.

uppfloat

Upper equivalence margin for the rate ratio, rate1 / rate2.

nobs_ratiofloat, optional

Sample size ratio, nobs2 = nobs_ratio * nobs1.

exposurefloat, optional

Exposure for each observation. Total exposure is nobs1 * exposure and nobs2 * exposure.

alphafloat in interval (0,1), optional

Significance level, e.g., 0.05, is the probability of a type I error, that is wrong rejections if the Null Hypothesis is true.

dispersionfloat, optional

Dispersion coefficient for quasi-Poisson. Dispersion different from one can capture over or under dispersion relative to Poisson distribution.

method_var{“score”, “alt”}, optional

The variance of the test statistic for the null hypothesis given the rates under the alternative, can be either equal to the rates under the alternative method_var="alt", or estimated under the constrained of the null hypothesis, method_var="score".

return_resultsbool, optional

If true, then a results instance with extra information is returned, otherwise only the computed power is returned.

Returns:

PowerEquivalenceResult or float

If return_results is False (default), then only the power is returned as a float. If return_results is True, then a PowerEquivalenceResult result object is returned; it behaves like the scalar power in numeric comparisons (e.g. assert_allclose), while also exposing std_null_low, std_null_upp, std_alt and other attributes.

References

[1]

Zhu, Haiyuan. 2017. “Sample Size Calculation for Comparing Two Poisson or Negative Binomial Rates in Noninferiority or Equivalence Trials.” Statistics in Biopharmaceutical Research, March. https://doi.org/10.1080/19466315.2016.1225594

[2]

Zhu, Haiyuan, and Hassan Lakkis. 2014. “Sample Size Calculation for Comparing Two Negative Binomial Rates.” Statistics in Medicine 33 (3): 376-87. https://doi.org/10.1002/sim.5947.

[3]

PASS documentation