在配对设计中条件优势比通常用来分析某种疾病与风险暴露因 素的关联强度,在医学研究中具有重要的临床意义.文章用4种方法来构造三项式抽样下 条件优势比的渐近置信区间,分别为:三项式下的Delta方法、对数变换 方法、基于Filler定理改进的方法和得分检验法,每种方法都各有其优缺点. 三项式抽样下构造条件优势比的置信区间是本文的一个创新点.根据区间 对条件优势比真值的覆盖率以及平均区间长度两个指标, 通过蒙特卡洛模拟结果对这4种区间估计方法进行评价.最后,文章通过 两个实证案例来直观展示4种区间估计方法的表现性能.

Conditional odds ratio is usually used to quantify the strength of the association between a given disease and a suspected exposure risk factor in matched-pair design. It has important clinical significance in medical research. In this paper four methods used to construct the asympotic confidence interval of conditional odds ratio under trinomial sampling, Delta method, log transformation method, an improved method based on Filler's theorem and score statistics method respectively. Each method has its own advantages and disadvantages. It is an innovation of this paper to consider the confidence interval of the conditional odds ratio under the trinomial sampling. We use Monte Carlo simulation to evaluate the four interval estimation methods based on the coverage of interval to conditional odds ratio and the average interval length. Finally, two empirical cases are used to show the different characteristics of four interval estimation methods.