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相关概念视频

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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Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Longitudinal Studies01:26

Longitudinal Studies

171
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
171
Outliers and Influential Points01:08

Outliers and Influential Points

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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
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相关实验视频

Updated: Jul 13, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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在密集的纵向数据中使用混合效应位置尺度模型检测有影响力的受试者.

Xingruo Zhang1, Donald Hedeker2

  • 1Department of Public Health Sciences, The University of Chicago, 5841 South Maryland Ave MC2000, Chicago, 60637, IL, USA. xrzhang@uchicago.edu.

BMC medical research methodology
|October 18, 2023
PubMed
概括

本研究引入了一个新的框架,使用混合效应位置尺度 (MELS) 建模来识别影响健康结果变化的受试者. 该方法有效地检测出有影响力的受试者,包括那些被标准回归模型遗漏的人.

关键词:
这是库克的距离.有影响力的数据.集中的纵向数据.混合效果的位置尺度模型.变量建模的变量建模.

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科学领域:

  • 纵向数据分析的数据分析.
  • 统计建模 统计建模
  • 生物统计学 生物统计学

背景情况:

  • 纵向健康结果需要共同建模平均值和可变性.
  • 标准的混合效应回归模型 (MRMs) 可以检测出影响结果位置的受试者.
  • 现有的方法缺乏方法来检测影响结果变化 (规模) 的受试者.

研究的目的:

  • 提出一种新的框架,用于检测在纵向健康结果的位置和规模上具有影响力的受试者.
  • 将影响分析扩展到标准混合效应回归模型 (MRM) 之外.
  • 提供一种全面的方法来检查对象对MELS模型组件的影响.

主要方法:

  • 应用混合效应位置尺度 (MELS) 建模.
  • 整合已有的影响力衡量标准,如库克距离和DFBETAS.
  • 制定一个框架,详细检查对象对模型匹配和系数估计的影响.

主要成果:

  • 模拟表明该框架在识别常见的纵向医疗保健场景中具有影响力的受试者方面具有很高的准确性 (超过99%).
  • 对健康行为研究的重新分析确定了4个有影响力的受试者.
  • 鉴定出有影响力的两个受试者在使用标准MRM时无法检测到.

结论:

  • 提出的基于MELS的框架成功地确定了传统MRM经常忽视的有影响力的主题.
  • 这种方法可以在单一模型中对所有数据进行全面分析,即使存在有影响力的受试者.
  • 研究人员可以利用这个框架来提高纵向健康结果分析的稳定性和准确性.