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《39 步》:一种用于分析能量摄入和支出数据的统计分析算法。

The '39 steps': an algorithm for performing statistical analysis of data on energy intake and expenditure.

机构信息

Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, AB24 2TZ, Scotland, UK.

出版信息

Dis Model Mech. 2013 Mar;6(2):293-301. doi: 10.1242/dmm.009860.

Abstract

The epidemics of obesity and diabetes have aroused great interest in the analysis of energy balance, with the use of organisms ranging from nematode worms to humans. Although generating energy-intake or -expenditure data is relatively straightforward, the most appropriate way to analyse the data has been an issue of contention for many decades. In the last few years, a consensus has been reached regarding the best methods for analysing such data. To facilitate using these best-practice methods, we present here an algorithm that provides a step-by-step guide for analysing energy-intake or -expenditure data. The algorithm can be used to analyse data from either humans or experimental animals, such as small mammals or invertebrates. It can be used in combination with any commercial statistics package; however, to assist with analysis, we have included detailed instructions for performing each step for three popular statistics packages (SPSS, MINITAB and R). We also provide interpretations of the results obtained at each step. We hope that this algorithm will assist in the statistically appropriate analysis of such data, a field in which there has been much confusion and some controversy.

摘要

肥胖症和糖尿病的流行引起了人们对能量平衡分析的极大兴趣,研究对象从线虫等微生物到人类不等。尽管获取能量摄入或消耗数据相对直接,但分析这些数据的最佳方法在过去几十年一直是一个争议点。在过去的几年中,人们对于分析此类数据的最佳方法已经达成共识。为了方便使用这些最佳实践方法,我们在此提出了一种算法,为分析能量摄入或消耗数据提供了一个逐步指导。该算法可用于分析来自人类或实验动物(如小型哺乳动物或无脊椎动物)的数据。它可以与任何商业统计软件包一起使用;但是,为了便于分析,我们为三个流行的统计软件包(SPSS、MINITAB 和 R)中的每一步都提供了详细的说明。我们还提供了在每个步骤中获得的结果的解释。我们希望该算法将有助于对该领域中存在很多混淆和一些争议的此类数据进行统计学上适当的分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adea/3597012/1a8bf32edfbf/DMM009860TB1.jpg

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