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

Hazard Ratio01:12

Hazard Ratio

664
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
664
Randomized Experiments01:13

Randomized Experiments

9.2K
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...
9.2K
Causality in Epidemiology01:21

Causality in Epidemiology

1.8K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.8K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

510
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,...
510
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

342
Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
342
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

323
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...
323

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

The hypoxic and acidic microenvironment created by GelMA hydrogels enhances the stem-like characteristics of lung cancer cells by inhibiting the MAPK signaling pathway.

Molecular and cellular biochemistry·2026
Same author

Geographic variation in eligibility and uptake of GLP-1 receptor agonists for obesity in US adults.

American journal of preventive cardiology·2026
Same author

Federated target trial emulation for time-to-event outcomes via POLARIS: Pooled-equivalent One-shot Likelihood Aggregation for Real-world Inference in Survival.

Research square·2026
Same author

Phenome-wide analysis of downstream health outcomes following second-line antidiabetic agent prescriptions in All of Us.

Nature communications·2026
Same author

Risk factors for complications after laparoscopic surgery in children with congenital choledochal cysts.

Translational pediatrics·2026
Same author

Patterns and Trends of Glucose-Lowering Therapy in Alzheimer's Disease and Related Dementias.

Diabetes, obesity & metabolism·2026

相关实验视频

Updated: Mar 6, 2026

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.3K

一个因果元分析框架,用于具有不平等随机化比率的临床试验.

Dazheng Zhang1,2, Bingyu Zhang1,3, Lu Li1,3

  • 1The Center for Health AI and Synthesis of Evidence (CHASE), https://ror.org/00b30xv10University of Pennsylvania, Philadelphia, PA, USA.

Research synthesis methods
|March 5, 2026
PubMed
概括

本研究引入因果元分析 (CMA),使用聚合数据进行可解释的治疗效果估计. CMA解决了标准方法的局限性,为不同的目标人群提供了准确的因果效应估计.

关键词:
有关因果关系的估计和估计临床试验是指临床试验中的临床试验.这是一个元分析.平均治疗效果的平均值.

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

15.4K

相关实验视频

Last Updated: Mar 6, 2026

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.3K
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

15.4K

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:

  • 分析综合了随机临床试验的证据,为医疗实践提供了信息.
  • 标准的元分析面临着诸如违反可运输性和非可折叠效应测量等挑战.
  • 对于可因果解释的元分析,通常需要个人参与者数据 (IPD).

研究的目的:

  • 提出一个因果元分析 (CMA) 框架,仅使用聚合数据.
  • 为了能够对各种目标人群进行因果解释和准确的治疗效果估计.
  • 在传统的元分析中解决混偏见和非合并性问题.

主要方法:

  • 开发了一个因果元分析 (CMA) 框架,利用聚合数据.
  • 针对不同目标人群 (ATE,ATT,ATC,ATO) 的治疗效果进行调整的权重.
  • 传统的元分析估计器和CMA之间的数学推导连接.

主要成果:

  • 拟议的CMA框架允许在没有IPD的情况下进行因果解释的治疗效果估计.
  • CMA为各种目标人群提供准确的估计,包括ATE,ATT,ATC和ATO.
  • 证明了Mantle-Haenszel元分析对CMA与ATO的等价性.

结论:

  • 因果元分析 (CMA) 为因果推断提供了一个强大的替代标准元分析.
  • 该CMA框架有效地处理可运输性和混偏差的问题.
  • 这种方法可以从聚合数据中更准确,更易于解释的综合证据.