Confidence Intervals for Ratios of Proportions in Stratified Bilateral Correlated Data

TL;DR

This paper introduces four new confidence interval methods for ratios of proportions in stratified bilateral data, demonstrating their effectiveness through simulations and a real data example.

Contribution

It proposes four novel confidence interval methods tailored for stratified bilateral data under Dallal's model, with the complete MLE-based score method showing robustness.

Findings

The complete MLE-based score CI performs robustly in simulations.

Monte Carlo studies validate the proposed methods.

Application to real data illustrates practical utility.

Abstract

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.