An improved estimator of the logarithmic odds ratio for small sample sizes using a Bayesian approach.

IF 1.2 4区 数学
Toru Ogura, Takemi Yanagimoto
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引用次数: 0

Abstract

The logarithmic odds ratio is a well-known method for comparing binary data between two independent groups. Although various existing methods proposed for estimating a logarithmic odds ratio, most methods estimate two proportions in each group independently and then estimate the logarithmic odds ratio using the two estimated proportions. When using a logarithmic odds ratio, researchers are more interested in the logarithmic odds ratio than proportions for each group. Parameter estimations, generally, incur random and systematic errors. These errors in initially estimated parameter may affect later estimated parameter. We propose a Bayesian estimator to directly estimate a logarithmic odds ratio without using proportions for each group. Many existing methods need to estimate two parameters (two proportions in each group) to estimate a logarithmic odds ratio; however, the proposed method only estimates one parameter (logarithmic odds ratio). Therefore, the proposed estimator can be closer to the population's logarithmic odds ratio than existing estimators. Additionally, the validity of the proposed estimator is verified using numerical calculations and applications.

使用贝叶斯方法的小样本量对数比值比的改进估计器。
对数优势比是比较两个独立组之间二进制数据的一种众所周知的方法。虽然已有各种方法提出了估计对数优势比,但大多数方法在每组中独立估计两个比例,然后使用这两个估计比例估计对数优势比。当使用对数优势比时,研究人员对对数优势比比对每组的比例更感兴趣。参数估计通常会产生随机和系统误差。初始估计参数的这些误差可能会影响以后的估计参数。我们提出了一个贝叶斯估计器来直接估计对数比值比,而不使用每组的比例。许多现有的方法需要估计两个参数(每组中两个比例)来估计对数优势比;然而,该方法只估计一个参数(对数比值比)。因此,所提出的估计量比现有的估计量更接近总体的对数比值比。此外,通过数值计算和应用验证了所提估计器的有效性。
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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics Mathematics-Statistics and Probability
CiteScore
2.30
自引率
8.30%
发文量
28
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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