statsmodels.stats.rates.tost_poisson_2indep¶
-
statsmodels.stats.rates.tost_poisson_2indep(count1, exposure1, count2, exposure2, low, upp, method=
'score'
, compare='ratio'
)[source]¶ Equivalence test based on two one-sided test_proportions_2indep
This assumes that we have two independent poisson samples.
The Null and alternative hypothesis for equivalence testing are
for compare = ‘ratio’
H0: rate1 / rate2 <= low or upp <= rate1 / rate2
H1: low < rate1 / rate2 < upp
for compare = ‘diff’
H0: rate1 - rate2 <= low or upp <= rate1 - rate2
H1: low < rate - rate < upp
- Parameters:¶
- count1
int
Number of events in first sample
- exposure1
float
Total exposure (time * subjects) in first sample
- count2
int
Number of events in second sample
- exposure2
float
Total exposure (time * subjects) in second sample
- low, upp
equivalence margin for the ratio or difference of Poisson rates
- method: string
TOST uses
test_poisson_2indep
and has the same methods.ratio:
‘wald’: method W1A, wald test, variance based on observed rates
‘score’: method W2A, score test, variance based on estimate under the Null hypothesis
‘wald-log’: W3A, uses log-ratio, variance based on observed rates
‘score-log’ W4A, uses log-ratio, variance based on estimate under the Null hypothesis
‘sqrt’: W5A, based on variance stabilizing square root transformation
- ‘exact-cond’: exact conditional test based on binomial distribution
This uses
binom_test
which is minlike in the two-sided case.
‘cond-midp’: midpoint-pvalue of exact conditional test
‘etest’ or ‘etest-score: etest with score test statistic
‘etest-wald’: etest with wald test statistic
diff:
‘wald’,
‘waldccv’
‘score’
‘etest-score’ or ‘etest: etest with score test statistic
‘etest-wald’: etest with wald test statistic
- count1
- Returns:¶
- results
instance
of
HolderTuple
class
The two main attributes are test statistic statistic and p-value pvalue.
- results
References
Gu, Ng, Tang, Schucany 2008: Testing the Ratio of Two Poisson Rates, Biometrical Journal 50 (2008) 2, 2008