Pradhan Vivek, Menon Sandeep, Das Ujjwal
Boston Scientific, 100 Boston Scientific Way, Marlborough, MA 01752, USA.
Pharm Stat. 2013 Jan-Feb;12(1):48-58. doi: 10.1002/pst.1551. Epub 2013 Jan 7.
Clinical trials often use paired binomial data as their clinical endpoint. The confidence interval is frequently used to estimate the treatment performance. Tang et al. (2009) have proposed exact and approximate unconditional methods for constructing a confidence interval in the presence of incomplete paired binary data. The approach proposed by Tang et al. can be overly conservative with large expected confidence interval width (ECIW) in some situations. We propose a profile likelihood-based method with a Jeffreys' prior correction to construct the confidence interval. This approach generates confidence interval with a much better coverage probability and shorter ECIWs. The performances of the method along with the corrections are demonstrated through extensive simulation. Finally, three real world data sets are analyzed by all the methods. Statistical Analysis System (SAS) codes to execute the profile likelihood-based methods are also presented.
临床试验通常将配对二项式数据用作其临床终点。置信区间经常被用于估计治疗效果。Tang等人（2009年）提出了在存在不完整配对二元数据的情况下构建置信区间的精确和近似无条件方法。在某些情况下，Tang等人提出的方法在预期置信区间宽度（ECIW）较大时可能会过于保守。我们提出了一种基于轮廓似然并采用杰弗里斯先验校正的方法来构建置信区间。这种方法生成的置信区间具有更好的覆盖概率和更短的ECIW。通过广泛的模拟展示了该方法及其校正后的性能。最后，所有方法都对三个真实世界数据集进行了分析。还给出了执行基于轮廓似然方法的统计分析系统（SAS）代码。
