Vollset S E, Hirji K F, Afifi A A
Section for Medical Informatics and Statistics, University of Bergen, Norway.
Biometrics. 1991 Dec;47(4):1311-25.
We compare six methods for constructing confidence intervals for a single parameter in stratified logistic regression. Three of these are based on inversion of standard asymptotic tests--namely, the Wald, the score, and the likelihood ratio tests. The other three are based on the exact distribution of the sufficient statistic for the parameter of interest. These include the traditional exact method of constructing confidence intervals, and two others, the mid-P and mean-P methods, which are modifications of this procedure that aim at reducing the conservative bias of the exact method. Using efficient algorithms, the six methods are compared by determination of their exact coverage levels in a series of conditional sample spaces. An incident case-control study of lung cancer in women is used to further illustrate the differences among the various methods. Computation of coverage functions is seen as a useful graphical diagnostic tool for assessing the appropriateness of different methods. The mid-P and the score methods are seen to have better coverage properties than the other four.
我们比较了在分层逻辑回归中为单个参数构建置信区间的六种方法。其中三种基于标准渐近检验的反演——即Wald检验、得分检验和似然比检验。另外三种基于感兴趣参数的充分统计量的精确分布。这些方法包括构建置信区间的传统精确方法，以及另外两种方法，即mid - P方法和mean - P方法，它们是对该过程的修改，旨在减少精确方法的保守偏差。通过高效算法，在一系列条件样本空间中确定六种方法的精确覆盖水平来进行比较。一项女性肺癌的病例对照研究被用于进一步说明各种方法之间的差异。覆盖函数的计算被视为评估不同方法适用性的有用图形诊断工具。结果表明，mid - P方法和得分方法比其他四种方法具有更好的覆盖特性。
