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A new confidence interval for the difference between two binomial proportions of paired data.
Significance (P<0.05) was analyzed using independent samples t tests, χ2 distribution, and sample tests for differences in binomial proportions.
The sample size is derived for a clinical trial design where two independent binomial proportions are compared within each stratum.
Zhou and Qin [2004. New intervals for the difference between two independent binomial proportions. J. Statist. Plann. Inference 123, 97 115; 2005.
Comparative analysis was performed by comparing binomial proportions and exact 95% confidence intervals (CI), with non-overlapping CIs considered significantly different.
This article presents a new approach to setting confidence intervals for effect measures under inverse sampling, using the variance estimates recovered from exact confidence limits for single negative binomial proportions.
Motivated by a study on comparing sensitivities and specificities of two diagnostic tests in a paired design when the sample size is small, we first derived an Edgeworth expansion for the studentized difference between two binomial proportions of paired data.
Binomial proportions and 95% confidence intervals were used to estimate response rates to therapy.
The performance was above chance levels and stable throughout the testing (binomial proportions test, p>0.05).
Based on available sample size, exact confidence limits for binomial proportions estimate the frequency of mutations investigated in the present study as ranging from 0% to 8% in individuals with probable CAA in the general population.
Therefore, as expected, M-scaling of whole images or larger fragments (50×50 pixel fragments, which subtended 0.89°×0.89° without magnification) did not significantly affect the animals' performance (binomial proportions test, p>0.05; data not shown).
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