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

What is Variation?01:14

What is Variation?

18.6K
Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
18.6K
Hazard Rate01:11

Hazard Rate

440
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...
440
Hazard Ratio01:12

Hazard Ratio

624
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...
624
Variation01:19

Variation

8.1K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
8.1K
Excess Pressure Inside a Drop and a Bubble01:13

Excess Pressure Inside a Drop and a Bubble

3.5K
The shape of a small drop of liquid can be considered spherical, neglecting the effect of gravity. This drop can further be considered as two equal hemispherical drops put together due to surface tension. The forces acting on the spherical drop are due to the pressure of the liquid inside the drop, the pressure due to air outside the drop, and the force due to the surface tension acting on the two hemispherical drops.
3.5K
Conservative Site-specific Recombination and Phase Variation02:53

Conservative Site-specific Recombination and Phase Variation

6.9K
Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
The recognition sites for Cre recombinase called LoxP...
6.9K

You might also read

Related Articles

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

Sort by
Same author

Surgery or radiotherapy for early-stage cancer study protocol for an emulated target trial of radical radiotherapy versus radical cystectomy, with either following neoadjuvant chemotherapy, for organ-confined muscle-invasive bladder cancer.

BMJ open·2026
Same author

An Overview and Recent Developments in the Analysis of Multistate Processes.

Statistics in medicine·2026
Same author

Comparison of common multiple imputation approaches: An application of logistic regression with an interaction.

Research methods in medicine & health sciences·2026
Same author

Minimally invasive surgical resection reduces one-year mortality, especially in high-risk colon cancer patients: an emulated trial.

EClinicalMedicine·2026
Same author

England's national cancer plan is more of a commercial strategy than a health policy.

BMJ (Clinical research ed.)·2026
Same author

Estimated effect of correcting inequalities in minimally invasive surgical resection in patients with colon cancer in England: a population-based study.

The Lancet. Oncology·2026
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 12, 2026

Blue-hazard-free Candlelight OLED
10:18

Blue-hazard-free Candlelight OLED

Published on: March 19, 2017

9.9K

Explained variation of excess hazard models.

Camille Maringe1, Maja Pohar Perme2, Janez Stare2

  • 1Cancer Survival Group, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.

Statistics in Medicine
|April 11, 2018
PubMed
Summary
This summary is machine-generated.

We introduce a new method, ranks explained (RE), to quantify how much variation in cancer patient survival is explained by different factors. This method accounts for competing risks and provides insights into cancer survival mechanisms.

Keywords:
excess hazard modelsexplained variation

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.1K
Demystifying Venous Excess Ultrasound (VExUS): Image Acquisition and Interpretation
05:49

Demystifying Venous Excess Ultrasound (VExUS): Image Acquisition and Interpretation

Published on: May 16, 2025

4.7K

Related Experiment Videos

Last Updated: Feb 12, 2026

Blue-hazard-free Candlelight OLED
10:18

Blue-hazard-free Candlelight OLED

Published on: March 19, 2017

9.9K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.1K
Demystifying Venous Excess Ultrasound (VExUS): Image Acquisition and Interpretation
05:49

Demystifying Venous Excess Ultrasound (VExUS): Image Acquisition and Interpretation

Published on: May 16, 2025

4.7K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Oncology

Background:

  • Detailed cancer patient data enables multivariable modeling of cancer-specific mortality.
  • Understanding the contribution of individual variables to survival models is crucial.

Purpose of the Study:

  • To adapt and apply the ranks explained (RE) measure for quantifying variation in survival within a relative survival data setting.
  • To assess the properties and comparability of the RE measure in accounting for competing risks.

Main Methods:

  • Adapted the ranks explained (RE) measure for relative survival data, incorporating life tables for competing risks.
  • Introduced weights for deaths to reflect their probability of being cancer-related.
  • Calculated RE at each event time, allowing for time-varying analysis from diagnosis.

Main Results:

  • The RE measure demonstrated reasonable properties and comparability between relative and cause-specific survival settings.
  • For lung and colon cancer patients, one-year post-diagnosis RE values were substantial, particularly for women.
  • Stage at diagnosis explained a significant portion of survival variation: 12.4% (lung) and 61.8% (colon) for men, respectively.

Conclusions:

  • The proportion of survival variation explained by prognostic factors is key to understanding cancer survival mechanisms.
  • The time-varying RE measure offers valuable insights into the influence patterns of strong predictors over time.
  • This approach enhances the interpretability of multivariable models in cancer survival analysis.