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相关概念视频

Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
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Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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相关实验视频

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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一个贝叶斯半参数级标量函数回归,使用仪表变量进行测量误差.

Roger S Zoh1, Yuanyuan Luan1, Lan Xue2

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, Indiana.

Statistics in medicine
|July 9, 2024
PubMed
概括

这项研究引入了一种新的贝叶斯方法,用于准确分析可穿戴设备的体育活动数据,改善我们对其与肥胖等健康结果的联系的理解.

关键词:
贝叶斯语 贝叶斯语 贝叶斯语 贝叶斯语能源支出 能源支出仪器变量是指仪器变量.测量时出现的测量误差身体活动 身体活动在函数上的标量.

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科学领域:

  • 生物统计学 生物统计学
  • 流行病学 流行病学
  • 可穿戴技术可穿戴技术

背景情况:

  • 可穿戴设备 (例如ActiGraph) 对于监测研究中的身体活动至关重要.
  • 准确评估身体活动对健康结果 (如肥胖) 的影响越来越重要.
  • 现有的标量对函数回归 (SoFR) 方法经常假设白噪声测量误差,可能低估参数.

研究的目的:

  • 开发一个非参数贝叶斯测量错误纠正的SoFR模型.
  • 为了放松当前SoFR模型中常见的限制性假设.
  • 为分析可穿戴设备数据及其与健康结果的关联提供一个强大的方法.

主要方法:

  • 开发了一个非参数贝叶斯SoFR模型,并进行了测量误差校正.
  • 采用了仪器变量方法,具有时间变化的偏差因子,偏离了GMM.
  • 整合了对修正的功能共变量的基于模型的分组,以加强解释.

主要成果:

  • 拟议的方法在模拟中显示出强大的有限样本特性.
  • 该方法允许灵活建模,而不对测量误差做严格假设.
  • 成功应用于国家健康和检查调查数据.

结论:

  • 新的贝叶斯SoFR模型有效地纠正功能共变量的测量误差.
  • 这种方法提高了评估身体活动与健康结果之间的关系的准确性.
  • 有助于更容易地解释体育活动模式及其对健康的影响.