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分层含结构零2×2列联表基于风险比的同时置信区间估计 被引量:1

Simultaneous Confidence Interval Estimation of Risk Ratios for Stratified 2×2 Table with Structure Zero
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摘要 对分层含结构零2×2列联表中风险比的同时置信区间估计问题,提出五种基于似然比统计量、Score统计量、Wald统计量和对数变换Wald统计量的同时置信区间及三种Bootstrap同时置信区间.并采用Bonferroni,Single-step adjusted MaxT和Single-step adjusted MinP三种多重检验过程计算临界值.通过同时置信区间覆盖概率、覆盖宽度和Mesial非覆盖概率与非覆盖概率之比(RNCP)的模拟研究比较了置信区间的统计性能.结果表明,MinP检验过程下基于Score统计量的同时置信区间具有令人满意的结果,即这个区间的覆盖概率非常接近置信水平,且Mesial非覆盖概率与非覆盖概率之比(RNCP)均在[0.4,0.6]之间.最后通过实例分析对所提方法进行验证. In order to study the simultaneous confidence intervals construction for risk ratios in stratified 2×2 tables with structural zero.Five simultaneous confidence interval estimators which based on the likelihood-ratio test statistic,Score test statistic,Wald test statistic and log-transformation Wald statistic and three bootstrap simultaneous confidence interval estimators were presented.Three procedures for calculating critical values based on the Bonferroni,Single-step adjusted MaxT and Single-step adjusted MinP multiple comparison procedures were developed.Simulation studies are conducted to evaluate the performance of the proposed confidence intervals in terms of coverage probability(CP),expected confidence widths(CW)and ratio of the mesial non-coverage probability to the probability(RNCP).The results show that the simultaneous confidence intervals based on Score statistic via the MinP methods behave satisfactorily in the sense that the coverage probabilities are close to the pre-specified confidence level,and the ratios of the mesial non-coverage to non-coverage probabilities lie in[0.4,0.6],are hence recommended.A real example was used to illustrate the proposed methodologies.
作者 江绍萍 JIANG Shao-ping(School of mathematics and Computer science,Yunnan Minzu University,Kunming 650500,China)
出处 《数理统计与管理》 CSSCI 北大核心 2023年第2期302-314,共13页 Journal of Applied Statistics and Management
基金 国家自然科学基金(11461083)。
关键词 风险比 同时置信区间 含结构零2×2列联表 覆盖概率 覆盖宽度 risk ratios simultaneous confidence intervals 2×2 tables with structural zero coverage probability(CP) confidence widths(CW)
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