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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

727
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...
727
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

769
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,...
769
Actuarial Approach01:20

Actuarial Approach

385
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
385
Hospitals-II00:59

Hospitals-II

1.3K
Hospitals provide inpatient and outpatient services. Inpatient services provide care to patients that stay in the hospital for an extended period, ranging from days to months. Examples of inpatient services include intensive care units, hospital wards, or surgeries. Outpatient services provide care to patients who come to a hospital for a diagnostic or treatment but do not stay overnight —for example, diagnostic tests, surgical procedures, or health education.
Nurses that work in...
1.3K
Hazard Ratio01:12

Hazard Ratio

740
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...
740
Cancer Survival Analysis01:21

Cancer Survival Analysis

855
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
855

You might also read

Related Articles

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

Sort by
Same author

Gait in childhood and adulthood in persons with myelomeningocele - a retrospective analysis.

BMC neurology·2026
Same author

Associations of body mass index and metabolic health with stroke risk in a large prospective cohort with time updated covariates.

Scientific reports·2026
Same author

Associations between snus use and concentrations of CRP, 25(OH)D and testosterone: a population-based study.

Scandinavian journal of clinical and laboratory investigation·2026
Same author

Developing and validating a frailty score based on patient-reported outcome 3 months after stroke: A Riksstroke-based study.

PloS one·2026
Same author

Health-related Quality of Life Outcomes of Salvage Metastasis-directed Treatment Versus Elective Nodal Treatment for Oligorecurrent Nodal Prostate Cancer: A Secondary Analysis of the Phase 2, Open-label PEACE V-STORM Randomized Trial.

European urology oncology·2026
Same author

SISAQOL-IMI consensus-based guidelines to design, analyse, interpret, and present patient-reported outcomes in cancer clinical trials.

The Lancet. Oncology·2025

Related Experiment Video

Updated: Apr 18, 2026

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

15.5K

Evaluating hospital performance based on excess cause-specific incidence.

Bart Van Rompaye1, Marie Eriksson, Els Goetghebeur

  • 1Department of Statistics, School of Business and Economics, Umeå University, Umeå, SE-901 87, Sweden; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, Ghent, 9000, Belgium.

Statistics in Medicine
|February 3, 2015
PubMed
Summary
This summary is machine-generated.

Evaluating hospital performance for specific care, like reducing stroke deaths, can use average excess cumulative incidence. This method accounts for patient mix, offering clear benchmarks for quality assurance and healthcare improvement.

Keywords:
competing risksexcess cumulative incidencehospital performance evaluationquality-of-carestroke

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

2.8K
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

11.1K

Related Experiment Videos

Last Updated: Apr 18, 2026

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

15.5K
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

2.8K
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

11.1K

Area of Science:

  • Healthcare quality assurance
  • Clinical performance evaluation
  • Public health research

Background:

  • Formal evaluation of hospital performance is crucial for healthcare quality.
  • Assessing specific care types requires robust performance metrics.
  • Reducing cause-specific events is a key goal in healthcare systems.

Purpose of the Study:

  • To propose a novel method for evaluating hospital performance.
  • To introduce average excess cumulative incidence for cause-specific events.
  • To establish intuitive benchmarks for hospital quality assessment.

Main Methods:

  • Developing a performance evaluation metric: average excess cumulative incidence.
  • Accounting for the center's observed patient mix.
  • Applying the method to cerebrovascular deaths after stroke in Swedish stroke centers.

Main Results:

  • The proposed method provides an intuitive interpretation of hospital performance.
  • Average excess cumulative incidence facilitates the determination of important benchmarks.
  • The study demonstrates the application using real-world stroke registry data.

Conclusions:

  • Average excess cumulative incidence is a valuable tool for hospital performance evaluation.
  • This metric aids in quality assurance for specific healthcare events.
  • The approach supports setting meaningful benchmarks for healthcare providers.