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Exact‐corrected confidence interval for risk difference in noninferiority binomial trials
Author(s) -
Hawila Nour,
Berg Arthur
Publication year - 2023
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13688
Subject(s) - confidence interval , binomial proportion confidence interval , statistics , mathematics , tolerance interval , estimator , cdf based nonparametric confidence interval , coverage probability , robust confidence intervals , binomial (polynomial) , credible interval , interval estimation , margin (machine learning) , sample size determination , nominal level , computer science , negative binomial distribution , machine learning , poisson distribution
A novel confidence interval estimator is proposed for the risk difference in noninferiority binomial trials. The proposed confidence interval, which is dependent on the prespecified noninferiority margin, is consistent with an exact unconditional test that preserves the type‐I error and has improved power, particularly for smaller sample sizes, compared to the confidence interval by Chan and Zhang. The improved performance of the proposed confidence interval is theoretically justified and demonstrated with simulations and examples. An R package is also distributed that implements the proposed methods along with other confidence interval estimators.