Introduction |
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Bilateral correlated data are often encountered in ophthalmologic (or
otolaryngologic) studies, in which each unit contributes information for paired
organs to the studies, and the measurements from such paired organs are
generally highly correlated. Various statistical methods have been developed to
tackle intra-class correlation on bilateral correlated data analysis. In
practice, it is important to adjust the effect of confounder on statistical
inference, since either ignoring the intra-class correlation or confounding
effect may lead to biased inference. In this article, we propose three test
procedures for testing common risk difference for stratified bilateral
correlated data in the basis of equal correlation model assumption. Five
interval estimation of common difference of two proportions are derived. The
performance of proposed test procedures and interval estimation is examined
through Monte Carlo simulation. The simulation results show that the score test
statistics outperforms other statistics in the sense that it produces robust
type I error with high power. Score confidence interval with respect to
score test statistics performs satisfactorily in terms of good coverage rate
with reasonable interval width. One example from an otolaryngologic study is
given to illustrate our methodologies.
References 1. Testing homogeneity of difference of two proportions for stratified correlated paired binary data 2. Common risk difference test and interval estimation of risk difference for stratified bilateral correlated data |
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