Confidence Interval Estimation of Proportion Near Zero or One: A Modeling Secondary Study

Background and Objectives: When computing a confidence interval for a binomial proportion p, one must choose an exact interval that has a coverage probability of at least 1-α for all values of p. In this study, we compared the confidence intervals of Clopper-Pearson, Wald, Wilson, and double ArcSin...

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Main Authors: S Hamzeh, AR Soltanian, J Faradmal
Format: Article
Language:fas
Published: Tehran University of Medical Sciences 2017-02-01
Series:مجله اپیدمیولوژی ایران
Subjects:
Online Access:http://irje.tums.ac.ir/article-1-5621-en.html
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author S Hamzeh
AR Soltanian
J Faradmal
author_facet S Hamzeh
AR Soltanian
J Faradmal
author_sort S Hamzeh
collection DOAJ
description Background and Objectives: When computing a confidence interval for a binomial proportion p, one must choose an exact interval that has a coverage probability of at least 1-α for all values of p. In this study, we compared the confidence intervals of Clopper-Pearson, Wald, Wilson, and double ArcSin transformation in terms of maintaining a constant nominal type I error. Methods: Simulations were used to compare four methods of estimating a confidence interval, including the Clopper-Pearson, Wald, Wilson, and double ArcSic. The data were generated from the binomial and Poison distribution with parameters p, n and µ=np, 1000 were produced . Type I error of each method was calculated per simulation. The above methods were used to estimate confidence intervals in a meta-analysis study. Results: The results of the simulation study showed that double ArcSin keep confidence interval at [0,1], but for some proportion has high type I error or low coverage probability. The Clopper–Pearson interval guarantees that the coverage probability is always equal to or above the nominal confidence level for any fixed p. Conclusion: This study showed that confidence interval estimations the Clopper-Pearson than other methods of calculating the type I error fixed and smaller.
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spelling doaj.art-65ee0448b5b34b60a65c4883e80898cb2022-12-21T23:14:14ZfasTehran University of Medical Sciencesمجله اپیدمیولوژی ایران1735-74892228-75072017-02-011245563Confidence Interval Estimation of Proportion Near Zero or One: A Modeling Secondary StudyS Hamzeh0AR Soltanian1J Faradmal2 کارشناسی ارشد گروه آمار زیستی و اپیدمیولوژی، دانشکده بهداشت، دانشگاه علوم پزشکی همدان، همدان، ایران دانشیار آمار زیستی، گروه آمار زیستی، مرکز تحقیقات مدل‌سازی بیماری‌های غیر واگیر، دانشکده بهداشت، دانشگاه علوم پزشکی همدان، همدان، ایران دانشیار آمار زیستی، گروه آمار زیستی، مرکز تحقیقات مدل‌سازی بیماری‌های غیر واگیر، دانشکده بهداشت، دانشگاه علوم پزشکی همدان، همدان، ایران Background and Objectives: When computing a confidence interval for a binomial proportion p, one must choose an exact interval that has a coverage probability of at least 1-α for all values of p. In this study, we compared the confidence intervals of Clopper-Pearson, Wald, Wilson, and double ArcSin transformation in terms of maintaining a constant nominal type I error. Methods: Simulations were used to compare four methods of estimating a confidence interval, including the Clopper-Pearson, Wald, Wilson, and double ArcSic. The data were generated from the binomial and Poison distribution with parameters p, n and µ=np, 1000 were produced . Type I error of each method was calculated per simulation. The above methods were used to estimate confidence intervals in a meta-analysis study. Results: The results of the simulation study showed that double ArcSin keep confidence interval at [0,1], but for some proportion has high type I error or low coverage probability. The Clopper–Pearson interval guarantees that the coverage probability is always equal to or above the nominal confidence level for any fixed p. Conclusion: This study showed that confidence interval estimations the Clopper-Pearson than other methods of calculating the type I error fixed and smaller.http://irje.tums.ac.ir/article-1-5621-en.htmlbinomial distributionproportionexact confidence intervalapproximate confidence interval
spellingShingle S Hamzeh
AR Soltanian
J Faradmal
Confidence Interval Estimation of Proportion Near Zero or One: A Modeling Secondary Study
مجله اپیدمیولوژی ایران
binomial distribution
proportion
exact confidence interval
approximate confidence interval
title Confidence Interval Estimation of Proportion Near Zero or One: A Modeling Secondary Study
title_full Confidence Interval Estimation of Proportion Near Zero or One: A Modeling Secondary Study
title_fullStr Confidence Interval Estimation of Proportion Near Zero or One: A Modeling Secondary Study
title_full_unstemmed Confidence Interval Estimation of Proportion Near Zero or One: A Modeling Secondary Study
title_short Confidence Interval Estimation of Proportion Near Zero or One: A Modeling Secondary Study
title_sort confidence interval estimation of proportion near zero or one a modeling secondary study
topic binomial distribution
proportion
exact confidence interval
approximate confidence interval
url http://irje.tums.ac.ir/article-1-5621-en.html
work_keys_str_mv AT shamzeh confidenceintervalestimationofproportionnearzerooroneamodelingsecondarystudy
AT arsoltanian confidenceintervalestimationofproportionnearzerooroneamodelingsecondarystudy
AT jfaradmal confidenceintervalestimationofproportionnearzerooroneamodelingsecondarystudy