Common risk difference test and interval estimation of risk difference for stratified bilateral correlated data

Bilateral correlated data are often encountered in medical researches such as ophthalmologic (or otolaryngologic) studies, in which each unit contributes information from paired organs to the data analysis, and the measurements from such paired organs are generally highly correlated. Various statist...

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Bibliographic Details
Published in:Statistical methods in medical research 2019-08, Vol.28 (8), p.2418-2438
Main Authors: Shen, Xi, Ma, Chang-Xing, Yuen, Kam C, Tian, Guo-Liang
Format: Article
Language:English
Subjects:
Bias
Biological organs
Computer simulation
Confidence intervals
Correlation analysis
Data analysis
Error detection
International trade
Medical research
Monte Carlo simulation
Organs
Risk
Simulation
Statistical analysis
Statistical methods
Statistical tests
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Summary:Bilateral correlated data are often encountered in medical researches such as ophthalmologic (or otolaryngologic) studies, in which each unit contributes information from paired organs to the data analysis, 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 very important to adjust the effect of confounder on statistical inferences, since either ignoring the intra-class correlation or confounding effect may lead to biased results. In this article, we propose three approaches for testing common risk difference for stratified bilateral correlated data under the assumption of equal correlation. Five confidence intervals of common difference of two proportions are derived. The performance of the proposed test methods and confidence interval estimations is evaluated by Monte Carlo simulations. The simulation results show that the score test statistic outperforms other statistics in the sense that the former has robust type I error rates with high powers. The score confidence interval induced from the score test statistic performs satisfactorily in terms of coverage probabilities with reasonable interval widths. A real data set from an otolaryngologic study is used to illustrate the proposed methodologies.
ISSN:0962-2802
1477-0334
DOI:10.1177/0962280218781988