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:翻译摘要:
分层双侧研究的置信区间(CI)方法利用班级内相关性来避免误导性结果。本文提出了四种CI方法(样本大小加权全局MLE基于Wald-type CI,完全MLE基于Wald-type CI,剖面似然CI和完全MLE基于分数CI),研究了在Dallal的班级内模型下分层双侧数据的比例比的CI与临床试验设计。进行了蒙特卡罗模拟,得出了完全MLE基于分数置信区间(CS)方法的稳健结果。最后,通过一个真实数据例子来说明所提出的四个CI方法。