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相关概念视频

Sampling Plans01:23

Sampling Plans

191
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
191
Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K
Randomized Experiments01:13

Randomized Experiments

7.0K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.0K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

201
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...
201
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

104
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
104
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

133
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
133

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相关实验视频

Updated: Jul 12, 2025

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
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基于个人水平预测因素的集群随机试验中对子组分析的考虑

Brian D Williamson1,2,3, R Yates Coley4,5, Clarissa Hsu6

  • 1Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA. brian.d.williamson@kp.org.

Prevention science : the official journal of the Society for Prevention Research
|October 28, 2023
PubMed
概括

在集群研究中分析治疗效应 (HTE) 的异质性是一项挑战. 需要个别级别的模型来通过个别特征来检测HTE,因为集群级别的分析往往缺乏力量.

关键词:
集群随机试验 集群随机试验生态学研究 生态学研究在健康方面存在差异.治疗效应的异质性 治疗效应的异质性小组分析分析子组分析.

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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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相关实验视频

Last Updated: Jul 12, 2025

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

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Published on: May 13, 2022

5.9K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

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科学领域:

  • 生物统计学 生物统计学
  • 流行病学 流行病学
  • 医疗保健服务研究 医疗服务研究

背景情况:

  • 评估分组中的差异性干预效应,称为治疗效应异质性 (HTE),对于政策和资源分配至关重要.
  • 虽然HTE分析对于个人级别的研究是可以理解的,但对于使用汇总结果的集群级别的研究,它不太清楚.
  • 个人层面的特征聚合到集群层面,对HTE分析构成独特的挑战.

研究的目的:

  • 在使用个体级特征时,调查在集群级研究中分析治疗效果异质性 (HTE) 的挑战.
  • 为了比较个人层面与集群层面模型在通过单个变量检测HTE方面的强度.

主要方法:

  • 进行了模拟研究,以评估不同的建模方法的力量.
  • 个人级别模型的性能与集群级别模型的性能进行了比较,使用汇总的个人特征.
  • 这些方法是使用现实世界研究在长期护理中心的COVID-19增剂疫苗接种率的方法.

主要成果:

  • 模拟结果表明,只有单个级别的模型具有足够的功率来通过单个级别的变量检测HTE.
  • 使用聚合变量的集群级模型表明,检测HTE的功率很低.
  • 该研究强调了在集群设计中使用聚合数据分析HTE的实际困难.

结论:

  • 当个体特征感兴趣时,个人级别模型对于稳健的HTE分析至关重要.
  • 依赖集群级模型和聚合数据可能导致HTE检测能力不足,错过了针对性干预的机会.
  • 需要仔细考虑研究设计和分析方法,以便在集群研究中准确评估HTE.