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

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, controlled...
Relative Risk01:12

Relative Risk

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...
Hazard Ratio01:12

Hazard Ratio

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 evaluating a...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Hazard Rate01:11

Hazard Rate

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...
Odds Ratio01:09

Odds Ratio

The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...

You might also read

Related Articles

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

Sort by
Same author

Pilot Study: Vancomycin Calcium Sulfate Beads in the Prevention of Recurrent Capsular Contracture of Breast Implants.

Plastic and reconstructive surgery. Global open·2026
Same author

Test-Negative Designs With Multiple Testing Sources.

Statistics in medicine·2026
Same author

RANDOMIZATION INFERENCE FOR CLUSTER-RANDOMIZED TEST-NEGATIVE DESIGNS WITH APPLICATION TO DENGUE STUDIES: UNBIASED ESTIMATION, PARTIAL COMPLIANCE, AND STEPPED-WEDGE DESIGN.

The annals of applied statistics·2025
Same author

Spatiotemporal effects on dengue incidence based on a large cluster randomized study.

Statistical methods in medical research·2025
Same author

Investigating Symptom Duration Using Current Status Data: A Case Study of Postacute COVID-19 Syndrome.

Epidemiology (Cambridge, Mass.)·2025
Same author

Test-Negative Designs with Multiple Testing Sources.

medRxiv : the preprint server for health sciences·2025
Same journal

Forging Ahead: The Need for Improved Representation in Academic Ophthalmology.

American journal of ophthalmology·2026
Same journal

Clinical Utility of Ultra-Widefield Swept-Source OCT for Intraocular Tumors: Comparison With Ultrasonography, SD-OCT, and MRI.

American journal of ophthalmology·2026
Same journal

Therapeutic Advances in Corneal Scar management: Topical Treatments, Mesenchymal Cell Therapy and Stromal Transplantation.

American journal of ophthalmology·2026
Same journal

Increased Risk for Ocular Surface Neoplasia in Recipients of Solid Organ Transplant.

American journal of ophthalmology·2026
Same journal

Aflibercept With vs Without Reduced-Fluence Photodynamic Therapy for Polypoidal Choroidal Vasculopathy: Optical Coherence Tomography Angiographic changes from a randomized clinical trial.

American journal of ophthalmology·2026
Same journal

Posterior Segment Risk Factors for Penetrating Keratoplasty Failure.

American journal of ophthalmology·2026
See all related articles

Related Experiment Video

Updated: Jun 20, 2026

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

Risk comparisons

Nicholas P Jewell1

  • 1School of Public Health & Department of Statistics, University of California, Berkeley, Berkeley, California 94611, USA. jewell@berkeley.edu

American Journal of Ophthalmology
|September 29, 2009
PubMed
Summary

No abstract available in PubMed .

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 20, 2026

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

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