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相关概念视频

Longitudinal Research02:20

Longitudinal Research

12.0K
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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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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Observational Studies01:11

Observational Studies

8.7K
Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
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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...
188
Cross-Sectional Research01:50

Cross-Sectional Research

11.4K
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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Study Designs in Epidemiology01:20

Study Designs in Epidemiology

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Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
280

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相关实验视频

Updated: Jul 24, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

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在前性研究中大规模数据协调.

Ke Pan, Lydia A Bazzano, Kalpana Betha

    American journal of epidemiology
    |July 5, 2023
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    此摘要是机器生成的。

    影响健康和发育的风险和暴露的孕前期分析 (PrePARED) 联盟协调了12项研究的数据,以创建孕前期健康研究的资源. 这一努力使得研究罕见的危险因素和怀孕事件成为可能.

    关键词:
    财团 财团 财团 财团 财团数据统一和数据协调.预先设想的时间 预先设想的时间怀孕 怀孕 怀孕 怀孕 怀孕怀孕并发症 怀孕并发症

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    科学领域:

    • 生殖健康 生殖健康
    • 流行病学 流行病学
    • 数据科学数据科学数据科学

    背景情况:

    • 怀孕前的健康对母亲和婴儿的结果至关重要.
    • 现有的队列数据是分散的,限制了全面的分析.
    • 影响健康和发育的风险和暴露的预孕期分析 (PrePARED) 联盟是为了弥补这个差距而建立的.

    研究的目的:

    • 描述PrePARED联盟的数据协调方法和结果.
    • 从多个前性队列研究中创建一个统一的个体级数据集.
    • 建立未来关于怀孕前暴露和结果研究的基础.

    主要方法:

    • 来自12项前性队列研究的个人层面数据.
    • 在数据集成方面采用了行人交叉路口编目和协调程序.
    • 定义指数怀孕为第一个超过20周妊娠的基线后怀孕.

    主要成果:

    • 114,762名妇女的统一数据,包括25531个指数怀孕.
    • 指数怀孕发生在1976年至2021年之间,中位数分娩年为2008年.
    • 怀孕前的特征包括60%的无妊娠,58%的大学教育,37%的超重/肥胖.

    结论:

    • 统一的数据有助于研究不常见的孕前风险因素和怀孕事件.
    • 预备联盟为生殖健康研究提供了宝贵的资源.
    • 这一协调努力支持未来在怀孕前健康方面的大规模分析.