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

Randomized Experiments01:13

Randomized Experiments

6.7K
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...
6.7K
Experimental Designs01:16

Experimental Designs

11.1K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
11.1K
Study Design in Statistics01:15

Study Design in Statistics

7.8K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
7.8K
Cluster Sampling Method01:20

Cluster Sampling Method

11.6K
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...
11.6K
Group Design02:01

Group Design

8.9K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
8.9K
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

171
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...
171

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

How should covariates be handled in randomized trials? Empirical evidence from 50 trials and recommendations for practice.

Journal of clinical epidemiology·2026
Same author

Doubly Robust Estimators of the Restricted Mean Time in Favor Estimands in Individual- and Cluster-Randomized Trials.

Statistics in medicine·2026
Same author

Comparing clinical outcomes of rFSH versus rFSH+HP-hMG in women undergoing in vitro fertilization-embryo transfer in real‑world practice: a retrospective study.

Scientific reports·2026
Same author

Fair and Robust Estimation of Heterogeneous Treatment Effects for Optimal Policies in Multilevel Studies.

Multivariate behavioral research·2026
Same author

Hydrophilic Polymer-Modified Poly(ε-caprolactone)/Hydroxyapatite Three-dimensional-Printed Scaffolds with Enhanced Hydrophilicity and Biodegradability for Regenerative Endodontic Applications.

Journal of endodontics·2026
Same author

On flexible covariate adjustment under covariate-constrained randomization.

Clinical trials (London, England)·2026
Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026
Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026
Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026
Same journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026
查看所有相关文章

相关实验视频

Updated: May 29, 2025

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

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

Published on: May 13, 2022

5.8K

对于集群随机化实验的模型-强大和高效的共变量调整.

Bingkai Wang1, Chan Park1, Dylan S Small1

  • 1The Statistics and Data Science Department of the Wharton School, University of Pennsylvania, Philadelphia, PA, USA.

Journal of the American Statistical Association
|February 6, 2025
PubMed
概括
此摘要是机器生成的。

这项研究为集群随机化实验引入了强大的统计方法,提高了共变量调整的准确性. 新的方法减少了偏见,并提高了在现实环境中对干预效应估计的可靠性.

关键词:
因果推理的原因推理.集群随机化试验是指集群随机化试验.同变量调整的调整.有效影响的功能是有效影响的功能.估计和估计是什么机器学习 机器学习

更多相关视频

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

14.4K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

相关实验视频

Last Updated: May 29, 2025

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

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

Published on: May 13, 2022

5.8K
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

14.4K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

科学领域:

  • 生物统计学 生物统计学
  • 流行病学 流行病学
  • 临床试验 临床试验

背景情况:

  • 集群随机化实验对于在现实环境中评估干预措施很常见.
  • 基于模型的共变量调整经常被使用,但如果模型被错误指定,可能会产生偏差.

研究的目的:

  • 开发可靠的统计方法,用于集群随机化实验中的共变量调整.
  • 解决现有的基于模型的方法的局限性,包括因错误规范和集群大小变化的潜在偏差.

主要方法:

  • 调整了通用估计方程和线性混合模型,使用加权g计算.
  • 提出了高效,三倍强大的估计器,允许灵活调整共变量,并考虑随机化后的集群大小变化.
  • 使用机器学习和参数模型进行麻烦函数估计的验证方法.

主要成果:

  • 建议的估计器在使用机器学习时是一致的,异常正常的,并且高效的.
  • 在使用参数模型时,估计器具有三倍的稳定性,可以防止不同的模型错误规范.
  • 通过模拟和真实世界的数据分析,证明了对现有方法的优越性.

结论:

  • 开发的方法在集群随机试验中提供了更可靠,更不偏的治疗效果估计.
  • 这些进展对于在复杂的,现实世界的医疗保健和公共卫生环境中准确评估干预至关重要.