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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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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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Cross-Sectional Research01:50

Cross-Sectional Research

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In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
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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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Applications of Life Tables01:22

Applications of Life Tables

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Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
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Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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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.
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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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[基于组的多轨迹模型在纵向数据研究中的应用和案例研究]

X Y Wang1, X B Sun1, Y M Ji1

  • 1Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan250012, China Institute for Medical Dataology, Shandong University, Jinan250002, China.

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi
|December 4, 2024
PubMed
概括

基于组的多轨迹模型 (GBMTM) 分析了使用智能设备的老年人的复杂健康数据. 这种方法揭示了多种生活方式因素如何影响不同人口子组的高血压轨迹.

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科学领域:

  • 生物统计学 生物统计学
  • 老年学是一门学科.
  • 医疗信息学 医疗信息学

背景情况:

  • 纵向队列可以追踪影响疾病的生物和行为因素.
  • 传统的对纵向数据的单变量分析不充分利用多变量信息.
  • 基于组的多轨迹模型 (GBMTM) 为多变量纵向数据提供了高级分析.

研究的目的:

  • 介绍并解释基于组的多轨迹模型 (GBMTM) 的原则.
  • 应用GBMTM来分析来自老年人健康管理研究的多变量纵向数据.
  • 使用GBMTM调查多个与生活相关的变量和高血压之间的关系.

主要方法:

  • 利用基于小组的多轨迹模型 (GBMTM) 来分析发育轨迹.
  • 使用来自健康管理研究的数据,涉及老年参与者的智能可穿戴设备.
  • 进行了多变量纵向数据分析,以确定人口子组及其高血压轨迹.

主要成果:

  • 证明了GBMTM能够根据其高血压轨迹在老年人群中识别不同的子组的能力.
  • 突出了多个与生活相关的变量对这些确定轨迹的影响.
  • 提供了对生活方式因素和老年人高血压发展之间的复杂相互作用的见解.

结论:

  • 在健康研究中,GBMTM是分析复杂的多变量纵向数据的强大工具.
  • 这项研究成功地应用了GBMTM来了解老年人高血压的决定因素.
  • 促进GBMTM在纵向队列研究中的更广泛采用,以加强健康结果分析.