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

Time-Series Graph00:54

Time-Series Graph

5.2K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.2K
Ogive Graph01:07

Ogive Graph

6.8K
An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
6.8K
Graphing Antiderivatives01:30

Graphing Antiderivatives

72
The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
72
Bar Graph01:07

Bar Graph

22.8K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
22.8K
Graphs of Functions01:30

Graphs of Functions

347
Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
347
Poisson's Ratio01:23

Poisson's Ratio

1.1K
Poisson's ratio is a material property that indicates their stress response. It explains the connection between the elongation or compression a material undergoes in the direction of an applied force and the contraction or expansion it experiences perpendicular to that force. When a slender bar is loaded axially, it stretches in the direction of the force and contracts laterally. Poisson's ratio is the negative ratio of this lateral contraction to the axial elongation. The negative sign...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Effects of Omega-3 Fatty Acid Treatment on Risk for Atrial Fibrillation: An Updated Meta-Analysis of 34 Trials including 114,326 Individuals.

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

Phenotypic and functional characterization of tumor-reactive T cells in malignant pleural effusions.

bioRxiv : the preprint server for biology·2025
Same author

PRAME is not a frequently expressed antigen in renal cell carcinoma.

BJUI compass·2025
Same author

Akt Activation With IPL344 Treatment for Amyotrophic Lateral Sclerosis: First in Human, Open-Label Study.

Muscle & nerve·2025
Same author

Decoy-resistant IL-18 reshapes the tumor microenvironment and enhances rejection by anti-CTLA-4 in renal cell carcinoma.

JCI insight·2024
Same author

GP100 expression is variable in intensity in melanoma.

Cancer immunology, immunotherapy : CII·2024
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Feb 5, 2026

Concurrent Quantification of Cellular and Extracellular Components of Biofilms
10:18

Concurrent Quantification of Cellular and Extracellular Components of Biofilms

Published on: December 10, 2013

8.8K

Graphing the Win Ratio and its components over time.

Dianne M Finkelstein1, David A Schoenfeld1

  • 1Massachusetts General Hospital and Harvard TH Chan School of Public Health, Biostatistics Center, 50 Staniford Street, Boston, MA 02114.

Statistics in Medicine
|September 13, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a graphical method to visualize the Win Ratio, addressing its dependence on follow-up times in clinical trials. The approach clarifies treatment comparisons using multiple endpoints, with available software for analysis.

Keywords:
Win Ratiocomposite testinterval censoredjoint testsurvival

More Related Videos

Extraction of the EPP Component from the Surface EMG
07:16

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

12.9K
Author Spotlight: Integrating 2D-HPLC-MS and Molecular Networking in Natural Medicine Analysis
07:50

Author Spotlight: Integrating 2D-HPLC-MS and Molecular Networking in Natural Medicine Analysis

Published on: December 8, 2023

1.2K

Related Experiment Videos

Last Updated: Feb 5, 2026

Concurrent Quantification of Cellular and Extracellular Components of Biofilms
10:18

Concurrent Quantification of Cellular and Extracellular Components of Biofilms

Published on: December 10, 2013

8.8K
Extraction of the EPP Component from the Surface EMG
07:16

Extraction of the EPP Component from the Surface EMG

Published on: December 16, 2009

12.9K
Author Spotlight: Integrating 2D-HPLC-MS and Molecular Networking in Natural Medicine Analysis
07:50

Author Spotlight: Integrating 2D-HPLC-MS and Molecular Networking in Natural Medicine Analysis

Published on: December 8, 2023

1.2K

Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Medical Data Analysis

Background:

  • Clinical trials frequently assess multiple outcomes to compare treatments.
  • The Finkelstein-Schoenfeld test (1999) and Win Ratio (2012) analyze treatment comparisons using primary or secondary outcomes.
  • The Win Ratio's sensitivity to follow-up time distributions was noted by Oakes (2016).

Purpose of the Study:

  • To propose a graphical method for representing the Win Ratio.
  • To visually demonstrate the impact of follow-up time on the Win Ratio estimate.
  • To display the contribution of individual endpoints to a composite outcome.

Main Methods:

  • Development of a novel graphical approach to visualize the Win Ratio.
  • Application of the graphical method to analyze clinical trial data.
  • Utilized the winRatioAnalysis software package available on CRAN.

Main Results:

  • The proposed graphical method effectively illustrates the Win Ratio's relationship with follow-up time.
  • The approach allows for clear visualization of how different endpoints contribute to the overall treatment effect.
  • Demonstrated applicability across diverse therapeutic areas including oncology, cardiology, and neurology.

Conclusions:

  • The graphical representation enhances the interpretability of the Win Ratio in clinical trials.
  • This method provides a transparent way to assess treatment effects considering time-dependent factors and composite endpoints.
  • The developed methods and software facilitate more robust analysis of multi-outcome clinical trial data.