برآورد فاصله اطمینان برای نسبت‌های نزدیک به صفر و یک: یک مطالعه ثانویه مدل سازی

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.