Confidence interval (CI) methods for stratified bilateral studies use intraclass correlation to avoid misleading results. In this article, we propose four CI methods (sample-size weighted global MLE-based Wald-type CI, complete MLE-based Wald-type CI, profile likelihood CI, and complete MLE-based score CI) to investigate CIs of proportion ratios to clinical trial design with stratified bilateral data under Dallal's intraclass model. Monte Carlo simulations are performed, and the complete MLE-based score confidence interval (CS) method yields a robust outcome. Lastly, a real data example is conducted to illustrate the proposed four CIs.

翻译：分层双边研究中的置信区间方法利用组内相关性来避免误导性结果。本文提出了四种置信区间方法（基于样本量加权全局最大似然估计的Wald型置信区间、基于完全最大似然估计的Wald型置信区间、剖面似然置信区间以及基于完全最大似然估计的分数置信区间），以探究Dallal组内模型下分层双边数据临床试验设计中比例比的置信区间。通过蒙特卡洛模拟发现，基于完全最大似然估计的分数置信区间法具有稳健性。最后，通过真实数据实例对所提出的四种置信区间方法进行了说明。