Accurate confidence intervals for proportion in studies with clustered binary outcome.

Clustered binary data are commonly encountered in many medical research studies with several binary outcomes from each cluster. Asymptotic methods are traditionally used for confidence interval calculations. However, these intervals often have unsatisfactory performance with regards to coverage for a study with a small sample size or the actual proportion near the boundary. To improve the coverage probability, exact Buehler's one-sided intervals may be utilized, but they are computationally intensive in this setting. Therefore, we propose using importance sampling to calculate confidence intervals that almost always guarantee the coverage. We conduct extensive simulation studies to compare the performance of the existing asymptotic intervals and the new accurate intervals using importance sampling. The new intervals based on the asymptotic Wilson score for sample space ordering perform better than others, and they are recommended for use in practice.