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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

129
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...
129
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

150
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
150
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

184
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
184
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

102
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
102
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

97
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.
97

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相关实验视频

Updated: Jun 5, 2025

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

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分析匹配的连续纵向数据:一篇综述

Margaux Delporte1, Marc Aerts2, Geert Verbeke1,2

  • 1I-BioStat, Ku Leuven, Leuven, Belgium.

Statistical methods in medical research
|December 11, 2024
PubMed
概括
此摘要是机器生成的。

对于配对的纵向医学数据,条件线性混合模型 (LMMs) 和多层模型比传统方法提供更高的精度. 考虑相关性和缺失数据对于准确分析至关重要.

关键词:
案例控制研究研究.纵向数据 纵向数据 纵向数据多层次分析的多层次分析.配对的数据是对应的数据.随机效应模型的随机效应模型

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相关实验视频

Last Updated: Jun 5, 2025

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

Published on: September 17, 2019

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科学领域:

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

背景情况:

  • 纵向数据,即随着时间的推移跟踪参与者,在医学研究中很常见.
  • 数据复杂性随着配对结构的增加,例如匹配的病例控制研究或参与者内部的双边测量.
  • 适当的统计建模对于有效分析如此复杂的纵向数据至关重要.

研究的目的:

  • 系统地审查和讨论对配对纵向数据的各种统计建模方法.
  • 用现实眼科和模拟病例控制研究来评估不同方法的性能.
  • 突出基于数据特征的模型选择的重要性,包括对内相关性和缺失数据.

主要方法:

  • 统计方法的系统审查,包括 (未) 配对的t测试,MANOVA,差异分数和线性混合模型 (LMM).
  • 将讨论的方法应用于眼科病例研究和模拟病例控制研究.
  • 专注于每个方法的比较优势和缺点,而不是数学复杂性.

主要成果:

  • 有条件的LMM和多级模型在处理配对的纵向数据方面表现出卓越的精度.
  • 该研究强调了考虑到对内相关性对分析结果的重大影响.
  • 缺乏数据机制的正确处理被证明是可靠结果的关键.

结论:

  • 条件LMM和多级模型是推用于分析配对的纵向数据,因为它们的精度.
  • 在选择分析模型时,研究人员必须仔细考虑数据结构,内对相关性和缺失数据.
  • 这些发现为复杂的医学研究环境中进行可靠的统计分析提供了实际指导.