Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

489
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,...
489
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

637
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...
637
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

648
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
648
Relative Risk01:12

Relative Risk

2.2K
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
2.2K
Hazard Ratio01:12

Hazard Ratio

644
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...
644
Hazard Rate01:11

Hazard Rate

458
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
458

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cellular-scale proximity labeling for recording cell spatial organization in mouse tissues.

Science advances·2023
Same author

Approximation of nearly-periodic symplectic maps via structure-preserving neural networks.

Scientific reports·2023
Same author

[Effectiveness of arthroscopic autologous iliac bone grafting with double-row elastic fixation for recurrent anterior shoulder dislocation with massive glenoid bone defect].

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery·2023
Same author

Identification of a novel binding inhibitor that blocks the interaction between hSCARB2 and VP1 of enterovirus 71.

Cell insight·2023
Same author

Mitigation of aircraft noise-induced vascular dysfunction and oxidative stress by exercise, fasting, and pharmacological α1AMPK activation: molecular proof of a protective key role of endothelial α1AMPK against environmental noise exposure.

European journal of preventive cardiology·2023
Same author

High-Dosage NMN Promotes Ferroptosis to Suppress Lung Adenocarcinoma Growth through the NAM-Mediated SIRT1-AMPK-ACC Pathway.

Cancers·2023

Related Experiment Video

Updated: Feb 20, 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

A Bayesian Meta-analysis Method for Estimating Risk Difference of Rare Events.

Yuanyuan Tang1, Qi Tang2, Yao Yu3

  • 1a Cardiovascular Research, Saint Luke's Mid America Heart Institute, Saint Luke's Hospital of Kansas City, Sain Luke's Health System , Kansas City , MO , USA.

Journal of Biopharmaceutical Statistics
|October 21, 2017
PubMed
Summary

A new Bayesian method, Beta prior BInomial model for Risk Differences (B-BIRD), improves risk difference estimation for rare events in binary outcomes. This Bayesian meta-analysis approach enhances precision by utilizing prior information on event rates.

Keywords:
Bayesianmeta-analysisrare eventrisk difference

More Related Videos

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

2.7K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K

Related Experiment Videos

Last Updated: Feb 20, 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
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

2.7K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.9K

Area of Science:

  • Biostatistics
  • Clinical Trial Analysis
  • Pharmacometrics

Background:

  • Bayesian meta-analysis is valuable for synthesizing clinical trial data.
  • Conventional methods struggle with rare events and zero counts in binary outcomes.
  • Existing methods often fail to leverage prior knowledge of event rates for improved precision.

Purpose of the Study:

  • To propose a novel Bayesian method for accurate risk difference estimation in rare event scenarios.
  • To develop a method that effectively incorporates prior information on rare event rates.
  • To address limitations of current meta-analysis techniques for binary data with low event frequencies.

Main Methods:

  • Introduction of the Beta prior BInomial model for Risk Differences (B-BIRD).
  • Application of B-BIRD to a dataset from 48 clinical trials on a type 2 diabetes drug.
  • Evaluation through simulation studies focusing on low event rate settings.

Main Results:

  • The B-BIRD method demonstrates robust performance in low event rate settings.
  • The proposed Bayesian approach effectively utilizes prior information for enhanced precision.
  • Successful illustration using a real-world clinical trial dataset.

Conclusions:

  • B-BIRD offers a superior approach for Bayesian meta-analysis of rare binary events.
  • The method enhances the precision of risk difference estimates by incorporating prior knowledge.
  • B-BIRD is a valuable tool for synthesizing safety and efficacy data in clinical research, particularly for type 2 diabetes drugs.