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

Cancer Survival Analysis01:21

Cancer Survival Analysis

448
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...
448
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

258
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,...
258
Actuarial Approach01:20

Actuarial Approach

132
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,...
132
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

196
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
196
Survival Curves01:18

Survival Curves

308
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
308
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

394
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
394

You might also read

Related Articles

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

Sort by
Same author

Artificial intelligence software to help detect fractures on X-rays in urgent care: An Early Value Assessment.

Health technology assessment (Winchester, England)·2026
Same author

A Sexual health and healthy relationships intervention for Further Education (SaFE): a synopsis of results from a pilot cluster randomised controlled trial including an assessment of the feasibility of record linkage and a health economic analysis.

Public health research (Southampton, England)·2026
Same author

A Scoping Review and Synthesis of Qualitative Evidence Reporting Stakeholders' Conceptualization of Child to Parent Violence and Abuse.

Trauma, violence & abuse·2026
Same author

The Effectiveness of Contact Tracing to Reduce Transmission of Infectious Diseases During Epidemic or Pandemic Response: Rapid Systematic Review.

JMIR public health and surveillance·2026
Same author

Effectiveness and cost-effectiveness of targeted and population screening for osteoporosis in women: scoping review.

Health technology assessment (Winchester, England)·2026
Same author

CALYPSO: Final Results of Savolitinib and Durvalumab Combination in Metastatic Papillary Renal Cancer.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026

Related Experiment Video

Updated: Sep 10, 2025

A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
06:38

A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies

Published on: April 12, 2017

13.7K

Structured Expert Elicitation to Inform Long-Term Survival Extrapolations in Advanced Renal Cell Carcinoma.

Dawn Lee1, Zain Ahmad2, James M G Larkin3

  • 1PenTAG, University of Exeter, University of Exeter Medical School, South Cloisters, St Luke's Campus, Exeter, EX1 2LU, UK. d.lee7@exeter.ac.uk.

Applied Health Economics and Health Policy
|August 27, 2025
PubMed
Summary

Structured expert elicitation provided valuable long-term survival estimates for advanced renal cell carcinoma treatments, informing cost-effectiveness analyses. This efficient method can guide decisions in rapidly evolving cancer care pathways.

More Related Videos

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

374
The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
12:22

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

33.7K

Related Experiment Videos

Last Updated: Sep 10, 2025

A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies
06:38

A Syngeneic Mouse Model of Metastatic Renal Cell Carcinoma for Quantitative and Longitudinal Assessment of Preclinical Therapies

Published on: April 12, 2017

13.7K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

374
The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers
12:22

The Use of Reverse Phase Protein Arrays RPPA to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

33.7K

Area of Science:

  • Oncology
  • Health Economics
  • Clinical Research Methodology

Background:

  • Lack of long-term data necessitates methods to assess clinical plausibility of extrapolations.
  • Structured expert elicitation (SEE) gathers expert judgments and uncertainties for this purpose.
  • Advanced renal cell carcinoma (RCC) treatment pathways are rapidly evolving.

Purpose of the Study:

  • To obtain expert estimates of long-term outcomes for advanced RCC treatments.
  • To inform cost-effectiveness analysis for National Institute for Health and Care Excellence (NICE) pathways.
  • To assess the feasibility of SEE in a complex, time-sensitive context.

Main Methods:

  • Utilized the STEER repository and MRC protocol for study design.
  • Recruited 9 oncologists from diverse UK settings.
  • Collected data on progression-free survival (PFS) and overall survival via online survey (roulette method) and aggregated using linear opinion pooling.

Main Results:

  • Experts projected similar PFS for immune oncology/tyrosine kinase inhibitor (TKI) combinations and TKI monotherapies from 5 years onward for first-line intermediate/poor-risk patients.
  • Nivolumab + ipilimumab was anticipated to yield the highest PFS among first-line therapies.
  • Expert estimates showed optimism compared to real-world evidence but pessimism compared to trial data.

Conclusions:

  • SEE is a pragmatic and efficient approach for informing long-term survival extrapolations in evolving treatment landscapes.
  • Demonstrated the feasibility of conducting expert elicitation for complex decision problems within an 8-week timeframe.