Advanced Statistics: Bootstrapping confidence intervals for statistics with "difficult" distributions
Advanced Statistics: Bootstrapping confidence intervals for statistics with "difficult" distributions
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DOI：
10.1197/j.aem.2004.11.018
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发表时间：
2005-04-01
影响因子：
4.4
通讯作者：
Lewis, RJ
中科院分区：
文献类型：
--
作者：
Haukoos, JS;Lewis, RJ
在报告研究结果时置信区间的使用急剧增加，现在许多科学期刊的编辑都要求或强烈推荐。许多资源描述了计算具有数学简单分布的统计置信区间的方法。计算难以用数学表示的分布的描述性统计的置信区间更具挑战性。引导程序是一种计算密集型统计技术，允许研究人员从数据中进行推断，而无需对数据或正在计算的统计数据做出强烈的分布假设。这使得研究人员能够估计没有简单抽样分布（例如中位数）的统计数据的置信区间。本文的目的是描述引导程序的概念，演示如何使用两种常用的统计软件包（SAS 和 Stata）估计近期临床研究中非正态分布数据的中位数置信区间和斯皮尔曼等级相关系数，并讨论引导程序的具体局限性。
The use of confidence intervals in reporting results of research has increased dramatically and is now required or highly recommended by editors of many scientific journals. Many resources describe methods for computing confidence intervals for statistics with mathematically simple distributions. Computing confidence intervals for descriptive statistics with distributions that are difficult to represent mathematically is more challenging. The bootstrap is a computationally intensive statistical technique that allows the researcher to make inferences from data without making strong distributional assumptions about the data or the statistic being calculated. This allows the researcher to estimate confidence intervals for statistics that do not have simple sampling distributions (e.g., the median). The purposes of this article are to describe the concept of bootstrapping, to demonstrate how to estimate confidence intervals for the median and the Spearman rank correlation coefficient for non-normally-distributed data from a recent clinical study using two commonly used statistical software packages (SAS and Stata), and to discuss specific limitations of the bootstrap.