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相关概念视频

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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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
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
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Longitudinal Studies01:26

Longitudinal Studies

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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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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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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...
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Sample Size Calculation01:19

Sample Size Calculation

3.8K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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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...
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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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对线性和日志线性增长的纵向干预研究的贝叶斯样本大小的确定.

Ulrich Lösener1, Mirjam Moerbeek2

  • 1Department of Methodology and Statistics, Utrecht University, Utrecht, Netherlands. u.c.losener1@uu.nl.

Behavior research methods
|July 28, 2025
PubMed
概括

本研究引入了一种用于测试贝叶斯假设的样本大小确定 (SSD) 的新方法,用于纵向研究. 它在多层模型中为SSD提供了一个R函数,这对于准确的试验设计至关重要.

关键词:
贝叶斯因子是一个贝叶斯因子.逻辑线性 逻辑线性纵向数据 纵向数据 纵向数据蒙特卡洛模拟的蒙特卡洛模拟多层次模型的多层次模型.动力 动力 动力 动力样本大小的确定 样本大小的确定

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科学领域:

  • 统计 统计 统计 统计
  • 生物统计学 生物统计学
  • 心理测量 心理测量 心理测量

背景情况:

  • 样本大小确定 (SSD) 对于高效,强大的研究至关重要,并且经常被伦理委员会和资助机构要求.
  • 基于NHST的SSD面临批评;使用贝叶斯因子的贝叶斯假设评估提供了一个替代方案.
  • 目前的贝叶斯式SSD工具仅限于简单的模型,不包括复杂的纵向数据,其中观察在个体内嵌套.

研究的目的:

  • 为使用纵向数据的多层模型在贝叶斯假设测试中提供样本大小确定 (SSD) 的工具.
  • 为在复杂的研究设计中实施贝叶斯式SSD提供必要的理论背景和实践示例.
  • 通过启用SSD用于嵌套数据结构来解决现有软件的局限性.

主要方法:

  • 该研究在贝叶斯框架内提出了基于模拟的SSD方法.
  • 它侧重于多层模型的应用,以处理纵向实验中固有的嵌套数据结构.
  • 开发了一个开源的R函数,以方便研究人员进行定制SSD模拟.

主要成果:

  • 开发的R函数允许研究人员在贝叶斯语境下对多层模型执行SSD.
  • 该工具支持纵向数据分析,如果观察不是独立的.
  • 这为设计具有复杂数据结构的研究提供了实用解决方案.

结论:

  • 这项工作为进行需要测试贝叶斯假设的纵向研究的研究人员提供了宝贵的资源.
  • 提供的R函数简化了复杂的多层模型的样本大小确定过程.
  • 使用此工具准确的SSD提高了涉及纵向数据的研究设计的严谨性和效率.