Interval Estimation of Relative Risks for Combined Unilateral and Bilateral Correlated Data

Abstract

Measurements are generally collected as unilateral or bilateral data in clinical trials or observational studies. For example, in ophthalmology studies, the primary outcome is often obtained from one eye or both eyes of an individual. In medical studies, the relative risk is usually the parameter of interest and is commonly used. In this article, we develop three confidence intervals for the relative risk for combined unilateral and bilateral correlated data under the equal dependence assumption. The proposed confidence intervals are based on maximum likelihood estimates of parameters derived using the Fisher scoring method. Simulation studies are conducted to evaluate the performance of proposed confidence intervals with respect to the empirical coverage probability, the mean interval width, and the ratio of mesial non-coverage probability to the distal non-coverage probability. We also compare the proposed methods with the confidence interval based on the method of variance estimates recovery and the confidence interval obtained from the modified Poisson regression model with correlated binary data.We recommend the score confidence interval for general applications because it best controls converge probabilities at the 95% level with reasonable mean interval width. We illustrate the methods with a real-world example.or bilateral tympanocentesis. The study compared children with effusion-free ears and those "improved"

Testing the Homogeneity of Differences between Two Proportions for Stratified Bilateral and Unilateral Data across Strata

“…Pei et al [10] studied ten test statistics to test the equality of two proportions and found that Rosner and Wald-type statistics based on dependency models and constrained maximum likelihood estimation perform satisfactorily for small to large samples. For general g ≥ 2 groups, Wang and Ma [11] worked on the combined bilateral and unilateral data and developed three types of confidence intervals for the relative ratio. Their simulation results show that score tests are always robust under different configurations with the median near the nominal type I error rate, and they yield higher powers than other test statistics, assuming the paired organs are not completely independent.…”