title: Asymptotic confidence interval construction for proportion difference in medical studies with bilateral data.
abstract: Bilateral dichotomous data are very common in modern medical comparative studies (e.g. comparison of two treatments in ophthalmologic, orthopaedic and otolaryngologic studies) in which information involving paired organs (e.g. eyes, ears and hips) is available from each subject. In this article, we study various confidence interval estimators for proportion difference based on Wald-type statistics, Fieller theorem, likelihood ratio statistic, score statistics and bootstrap resampling method under the dependence or/and independence models for bilateral binary data. Performance is evaluated with respect to the coverage probability and expected width via simulation studies. Our empirical results show that (1) ignoring the dependence feature of bilateral data could lead to severely incorrect coverage probabilities; and (2) Wald-type, score-type and bootstrap confidence intervals based on the dependence model perform satisfactorily for small to large sample sizes in the sense that their empirical coverage probabilities are close to the pre-specified nominal confidence level and are hence recommended. A real data from an otolaryngologic study is used to illustrate the proposed methods.
authors.affiliation: Department of Statistics, Yunnan University, Kunming 650091, PR China. nstang@ynu.edu.cn
authors.forename: Nian-Sheng
authors.initials: NS
authors.lastname: Tang
authors.forename: Shi-Fang
authors.initials: SF
authors.lastname: Qiu
authors.forename: Man-Lai
authors.initials: ML
authors.lastname: Tang
authors.forename: Yan-Bo
authors.initials: YB
authors.lastname: Pei
mesh_terms: D000900:Anti-Bacterial Agents / Q000627:therapeutic use; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D007223:Infant; D016013:Likelihood Functions; D015233:Models, Statistical; D010034:Otitis Media with Effusion / Q000188:drug therapy; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data
chemical_list: D000900:Anti-Bacterial Agents