Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Stratified Sampling Method01:16

Stratified Sampling Method

14.5K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures 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 stratified sample, divide the population into groups called strata and then take a...
14.5K
Sampling Plans01:23

Sampling Plans

889
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...
889
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

178
Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
178
Randomized Experiments01:13

Randomized Experiments

8.8K
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...
8.8K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

551
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...
551
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

359
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...
359

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Powerful Rare-Variant Association Analysis of Secondary Phenotypes.

Genetic epidemiology·2024
Same author

<i>Campanophyllummicrosporum</i> (Agaricales, Agaricomycetes), <i>Caloceramultiramosa</i>, and <i>Dacrymycesnaematelioides</i> (Dacrymycetales, Dacrymycetes), three new species from Yunnan Province, southwestern China.

MycoKeys·2024
Same author

Wide-range and high-accuracy wireless sensor with self-humidity compensation for real-time ammonia monitoring.

Nature communications·2024
Same author

BertSNR: an interpretable deep learning framework for single-nucleotide resolution identification of transcription factor binding sites based on DNA language model.

Bioinformatics (Oxford, England)·2024
Same author

Exceptionally High-<i>g</i><sub>lum</sub> Circularly Polarized Lasers Empowered by Strong 2D-Chiroptical Response in a Host-Guest Supramolecular Microcrystal.

Journal of the American Chemical Society·2024
Same author

Effect of Bedside Ultrasound-Guided Versus Fluoroscopy-Guided Transvenous Cardiac Temporary Pacing in Children with Bradyarrhythmia.

Pediatric cardiology·2024

相关实验视频

Updated: Jan 14, 2026

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

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.0K

适应性分层抽样设计在为期两阶段的研究中,用于平均因果效应估计.

Min Zeng1,2, Qiyu Wang1,3, Zijian Sui2

  • 1Department of Biostatistics, City University of Hong Kong, Hong Kong, 999077, China.

Biometrics
|October 27, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了自适应分层采样设计 (AdaStrat),以从观测数据中更有效地推断因果关系. 在两阶段研究中,AdaStrat最大限度地减少了混偏差,并改善了平均因果效应 (ACE) 估计.

关键词:
有关因果推理的推理.缺少的混因子采样设计 采样设计阶层化战略的分层化策略

更多相关视频

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.4K
Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

35.8K

相关实验视频

Last Updated: Jan 14, 2026

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

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.0K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.4K
Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

35.8K

科学领域:

  • 统计 统计 统计 统计
  • 流行病学 流行病学
  • 生物标志物研究 生物标志物研究

背景情况:

  • 对因果推断的观察数据分析受到混效应的挑战.
  • 昂贵的混数据 (例如,遗传生物标志物,医学成像) 限制了传统研究.
  • 两阶段研究通过收集有关主题子集的昂贵数据,提供了一种资源高效的方法.

研究的目的:

  • 为高效的因果推断提出一个自适应分层采样设计 (AdaStrat).
  • 为了最大限度地减少固定第二阶段样本大小内的平均因果效应 (ACE) 估计器的差异.
  • 在两阶段研究中改进现有的固定分层采样设计.

主要方法:

  • 开发了一种适应性分层采样设计 (AdaStrat) 用于两相研究.
  • 利用试点数据与昂贵的混措施来创建分层和采样策略.
  • 应用了AdaStrat策略来选择第二阶段对象进行昂贵的混测量.

主要成果:

  • 与前置分层设计相比,AdaStrat在ACE估计方面表现出更高的效率.
  • 模拟研究表明,AdaStrat的性能优于固定分层采样 (FixStrat),相对效率提高了20-30%.
  • 使用英国生物库数据验证了AdaStrat的有效性.

结论:

  • 在双相观察性研究中,AdaStrat提供了一种更有效的因果推理方法.
  • AdaStrat的自适应性优化了资源分配,从而使成本高昂的数据收集更加混乱.
  • AdaStrat提供了一个统计学上严格且实际上有效的解决方案,用于观察性研究中的混.