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相关概念视频

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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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 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...
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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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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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缺失的不是随机密集的纵向数据与动态结构方程模型.

Daniel McNeish1

  • 1Department of Psychology, Arizona State University.

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概括
此摘要是机器生成的。

密集的纵向研究往往有缺失的数据. 一种新的动态结构方程模型方法有效地处理缺失的非随机 (MNAR) 数据,改进敏感健康行为的分析.

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科学领域:

  • 心理学研究方法 心理学研究方法
  • 纵向数据分析的数据分析.
  • 统计建模 统计建模

背景情况:

  • 强烈的纵向设计能够捕捉到快速变化的情绪,影响和行为.
  • 在这些研究中,高频率的数据收集导致不可避免的缺失数据.
  • 在动态结构方程模型 (DSEM) 中缺失数据的现有研究是有限的,通常假设数据是随机缺失的 (MAR).

研究的目的:

  • 解决在动态结构方程模型 (DSEM) 中缺少非随机 (MNAR) 数据的挑战.
  • 提出和评估一种新的方法来处理密集的纵向研究中的MNAR数据,特别是对于敏感的结果.
  • 为使用复杂缺失数据模式的DSEM的研究人员提供实用方法.

主要方法:

  • 在DSEM框架中嵌入Diggle-Kenward类型的MNAR模型.
  • 将拟议的方法应用于一个充满动机的暴饮暴食障碍示例,其中包含自我报告的过度饮食数据.
  • 进行模拟研究,以评估拟议的MNAR模型对连续和二进制结果的性能.

主要成果:

  • 拟议的DSEM方法有效地处理MNAR数据,在相关场景中表现优于标准MAR模型.
  • 该方法被证明是适用的,并且相对容易在像Mplus.com这样的统计软件中实现.
  • 模拟结果证明了模型对连续和二进制结果变量的实用性.

结论:

  • 带有嵌入式MNAR组件的拟议的DSEM提供了一个强大的解决方案,用于分析具有缺失的密集纵向数据.
  • 这种方法对于精确建模可能存在MNAR的敏感行为至关重要.
  • 研究人员可以通过采用这种方法来处理DSEM中复杂的缺失数据来提高他们的发现的有效性.