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相关概念视频

Longitudinal Research02:20

Longitudinal Research

11.8K
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...
11.8K
Longitudinal Studies01:26

Longitudinal Studies

128
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...
128
Multiple Regression01:25

Multiple Regression

2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

151
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
151
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

90
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
90
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

90
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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相关实验视频

Updated: May 31, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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纵向数据的多重推算:一个教程

Rushani Wijesuriya1,2, Margarita Moreno-Betancur1,2, John B Carlin1,2

  • 1Clinical Epidemiology & Biostatistics (CEBU), Murdoch Children's Research Institute, Parkville, Australia.

Statistics in medicine
|January 23, 2025
PubMed
概括
此摘要是机器生成的。

在纵向研究中处理缺少的数据需要考虑个别聚类. 多重归算 (MI) 方法必须与分析模型保持一致,但目前的方法很复杂. 本教程回顾了用于集群纵向数据的可访问MI技术.

关键词:
聚类数据是聚类数据.完全有条件的规范规范.联合建模 联合建模纵向数据 纵向数据 纵向数据缺失的数据 缺失的数据多重的归算是多重的归算.

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

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科学领域:

  • 医学研究方法学 医学研究方法学
  • 生物统计学 生物统计学
  • 纵向数据分析 纵向数据分析

背景情况:

  • 纵向研究随着时间的推移收集重复的测量,需要分析方法来解释个人内相关的观察结果.
  • 缺少数据是一个常见的挑战,特别是在纵向研究中,参与者 attrition 可以导致不完整的数据集.
  • 多重归算 (MI) 是处理缺失数据的标准技术,但需要仔细考虑归算模型与分析模型的兼容性.

研究的目的:

  • 对不完整的纵向数据进行现有多重推算 (MI) 方法的审查,特别是针对聚类个体.
  • 强调在纵向研究中将归算模型与分析模型对齐的重要性.
  • 为实施这些MI方法提供实际指导和可重复的代码 (R和Stata).

主要方法:

  • 对适用于集群个体纵向数据的多重推算 (MI) 技术的审查.
  • 讨论归算策略,包括将重复测量作为不同的变量,并使用一般化的线性混合归算模型.
  • 使用R和Stata代码应用到现实世界的案例研究的说明性示例.

主要成果:

  • 现有的MI方法用于纵向数据,虽然有效,但往往涉及复杂的数据操纵和先进的程序,限制其采用.
  • 该教程表明,适当的MI技术可以在集群的纵向设置中成功处理缺失的数据.
  • 有代码的实施指南有助于在医学研究中应用这些方法.

结论:

  • 对纵向数据的准确分析需要推算模型,这些模型反映了个体内观察的集群性质.
  • 尽管存在实施方面的挑战,但仍有可访问的MI方法用于处理不完整的纵向数据,从而提高了研究有效性.
  • 这项工作提供了实用工具和审查,以鼓励在纵向医学研究中使用适当的MI技术.