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Interval Estimation of the Proportion Ratio in Repeated Binary Measurements Under a Stratified Randomized Clinical Trial with Noncompliance

机译:在不合规的分层随机临床试验中,重复二元测量的比例比的区间估计

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The proportion ratio (PR) of a positive response between an experimental treatment and a standard treatment (or placebo) is often used to measure the relative treatment efficacy in a randomized clinical trial (RCT). For ethical reasons, it is almost inevitable to encounter some patients not complying with their assigned treatment. Furthermore, when there are confounders in a RCT or meta-analysis, we commonly employ stratified analysis to control the confounding effects on interval estimation of the PR. On the basis of a general risk multiplicative model, we focus our discussion on interval estimation of the PR in repeated binary data under a stratified RCT with noncompliance. We develop seven asymptotic closed-form interval estimators for the PR. We apply Monte Carlo simulation to study the finite-sample performance of these interval estimators in a variety of situations. We note that the two interval estimators with the logarithmic transformation based on the commonly used weighted least squares (WLS) approach can be liberal, while the three interval estimators with the Mantel-Haenszel (MH) weight derived from various methods can consistently perform well. We also note that the two estimators with the estimated optimal weight defined in the context using Fieller's Theorem and a randomization-based approach may not necessarily produce a confidence interval preferable to the MH-type interval estimators for the PR with respect to accuracy and precision.View full textDownload full textKey WordsBinary repeated measurements, Coverage probability, Interval estimation, Noncollapsibility, Proportion ratio, Randomization-based approach, Stratified analysisRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/10543406.2010.508139
机译:实验治疗和标准治疗(或安慰剂)之间阳性反应的比例(PR)通常用于衡量随机临床试验(RCT)的相对治疗功效。出于道德原因,几乎不可避免地会遇到一些患者未遵守其指定治疗的情况。此外,当在RCT或荟萃分析中存在混杂因素时,我们通常采用分层分析来控制对PR间隔估计的混杂影响。在一般风险乘法模型的基础上,我们将讨论的重点放在不合规的分层RCT下重复二元数据中PR的区间估计。我们为PR开发了七个渐近闭式区间估计器。我们应用蒙特卡洛模拟来研究这些区间估计器在各种情况下的有限样本性能。我们注意到,基于常用加权最小二乘(WLS)方法进行对数变换的两个区间估计量可以是自由的,而具有从各种方法得出的Mantel-Haenszel(MH)权重的三个区间估计量可以始终如一地表现良好。我们还注意到,使用Fieller定理和基于随机的方法在上下文中定义了具有估计最佳权重的两个估计量,就准确性和精度而言,对于PR而言,不一定会产生优于MH型间隔估计量的置信区间。查看全文下载全文关键词二进制重复测量,覆盖率,区间估计,非可折叠性,比例,基于随机化的方法,分层分析相关var addthis_config = { ,delicious,linkedin,facebook,stumbleupon,digg,google,more“,发布编号:” ra-4dff56cd6bb1830b“};添加到候选列表链接永久链接http://dx.doi.org/10.1080/10543406.2010.508139

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