对维生素D补充剂的膨胀效应估计是由常见的元分析错误驱动的.
1Duke University, Department of Evolutionary Anthropology, Durham, North Carolina, USA.
Journal of the International Society of Sports Nutrition
|October 7, 2024
概括
重新分析维生素D和运动力量的元分析显示了计算错误. 经过校正的分析显示,维生素D补充对运动员没有显著的强度益处.
科学领域:
- 运动营养 运动营养
- 运动生理学 运动生理学
- 证据综合 证据综合
背景情况:
- 以前的元分析已经探索了维生素D补充剂对运动表现的影响.
- 汉等人的一项元分析. (2019) 报告了一种显著的效果大小,有利于维生素D获得强度结果.
- 在最初的研究中,人们对分析方法和效果大小计算提出了担忧.
研究的目的:
- 批判性地评估汉等人使用的元分析方法和效果大小计算. (2019年) 的时间.
- 为了重新分析来自Han等人的数据. (2019) 使用修正后的元分析技术.
- 为提供维生素D对运动力量的影响的最新和准确估计.
主要方法:
- 在最初的元分析中发现的错误包括错误的效果大小指标和对标准错误和相关观察的不适当处理.
- 为了解释这些错误,使用修正后的元分析程序重新分析了数据.
- 重复分析模型的统计特征使用道图和Cochrane的Q测试进行了评估.
主要成果:
- 重新分析结果显示,维生素D补充剂对强度的综合效应估计值 (SMD = 0.16; p = 0.43) 较小且在统计学上无意义.
- 修正后的模型表现出更好的统计特性,通过对称的漏斗图和无意义的科克莱恩Q测试 (p = 0.41) 表示.
- 这些发现与最初研究报告的显著效应大小形成鲜明对比.
结论:
- 准确和有效的效果估计对于可靠的体育营养元分析至关重要.
- 这次重新分析强调了元分析计算中常见的错误,这些错误可能会影响解释.
- 经过校正的结果表明,维生素D补充剂不会显著提高运动员的强度结果.
相关概念视频
Study Designs in Epidemiology
188
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
188
What is an Experiment?
10.5K
An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
10.5K
The Placebo Effect
6.1K
The placebo effect occurs when people's expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.
6.1K
Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
123
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
123
Accuracy and Errors in Hypothesis Testing
180
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
180


