Jiang Yannan, Scott Alastair, Wild Chris J
Department of Statistics, The University of Auckland, Private Bag 92019, Auckland, New Zealand.
Stat Med. 2009 Jan 30;28(2):194-204. doi: 10.1002/sim.3474.
It is not uncommon for a continuous outcome variable Y to be dichotomized and analysed using logistic regression. Moser and Coombs (Statist. Med. 2004; 23:1843-1860) provide a method for converting the output from a standard linear regression analysis using the original continuous outcome Y to give much more efficient inferences about the same odds-ratio parameters being estimated by the logistic regression. However, these results apply only to prospective studies. This paper follows up Moser and Coombs by providing an efficient linear-model-based solution for data collected using case-control studies. Gains in statistical efficiency of up to 240 per cent are obtained even with small to moderate odds ratios. Differences in design efficiency between case-control and prospective sampling designs are found to be much smaller, however, when linear-model-based analyses are being used than they are when logistic regression analyses are being used.
连续结果变量Y被二分并使用逻辑回归进行分析的情况并不少见。莫泽和库姆斯(《统计医学》,2004年;23:1843 - 1860)提供了一种方法,可将使用原始连续结果Y进行的标准线性回归分析的输出进行转换,以便对逻辑回归所估计的相同优势比参数做出更有效的推断。然而,这些结果仅适用于前瞻性研究。本文延续莫泽和库姆斯的研究,为使用病例对照研究收集的数据提供了一种基于线性模型的有效解决方案。即使对于小到中等的优势比。也能获得高达240%的统计效率提升。然而,发现当使用基于线性模型的分析时,病例对照抽样设计和前瞻性抽样设计之间的设计效率差异比使用逻辑回归分析时要小得多。