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

313
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...
313
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

67
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,...
67
Actuarial Approach01:20

Actuarial Approach

50
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,...
50
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

4.8K
Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
4.8K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

107
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...
107
Targeted Cancer Therapies02:57

Targeted Cancer Therapies

7.4K
The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
There are several types of targeted therapies against...
7.4K

You might also read

Related Articles

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

Sort by
Same author

Expert opinion in decision-making: a systematic review of methods and the INTEGRITY framework for incorporating expert consultation into research.

Research integrity and peer review·2026
Same author

An App-Based Behavioral Support Intervention Promoting Physical Activity (APPROACH) in Patients Diagnosed With Breast, Prostate, or Colorectal Cancer: Protocol for a Randomized Controlled Trial.

JMIR research protocols·2026
Same author

Assumptions Matter: The Long-Term Cost Analysis of IsaVRd vs DVRd.

Journal of health economics and outcomes research·2025
Same author

Augmented two-stage estimation for treatment switching in oncology trials: Leveraging external data for improved precision.

Statistical methods in medical research·2025
Same author

Cost-effectiveness of cetuximab-containing regimens for squamous cell carcinoma of the head and neck in Italy.

Future oncology (London, England)·2025
Same author

Correction: De Novo Cost‑Effectiveness Model Framework for Nonalcoholic Steatohepatitis-Modeling Approach and Validation.

PharmacoEconomics·2025

Related Experiment Video

Updated: May 21, 2025

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

195

Use of external data to inform overall survival extrapolation in NICE technology appraisals for oncology drugs.

Audrey Petitjean1, Huiyu Shang2, Ash Bullement3,4

  • 1Sanofi, Lyon, France.

Journal of Medical Economics
|May 20, 2025
PubMed
Summary

External data, including real-world data, is increasingly used for overall survival (OS) estimation in oncology single-technology appraisals (STAs). NICE generally accepts this external evidence, but robust sensitivity analyses and clear justifications are essential.

More Related Videos

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients
07:42

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

Published on: February 7, 2021

4.8K
Ex Vivo Treatment Response of Primary Tumors and/or Associated Metastases for Preclinical and Clinical Development of Therapeutics
08:29

Ex Vivo Treatment Response of Primary Tumors and/or Associated Metastases for Preclinical and Clinical Development of Therapeutics

Published on: October 2, 2014

14.6K

Related Experiment Videos

Last Updated: May 21, 2025

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

195
Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients
07:42

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

Published on: February 7, 2021

4.8K
Ex Vivo Treatment Response of Primary Tumors and/or Associated Metastases for Preclinical and Clinical Development of Therapeutics
08:29

Ex Vivo Treatment Response of Primary Tumors and/or Associated Metastases for Preclinical and Clinical Development of Therapeutics

Published on: October 2, 2014

14.6K

Area of Science:

  • Health economics and outcomes research
  • Oncology drug appraisal
  • Health technology assessment

Background:

  • National Institute for Health and Care Excellence (NICE) single-technology appraisals (STAs) evaluate oncology drugs.
  • Accurate estimation of overall survival (OS) is critical for these appraisals.
  • External evidence is often required to supplement trial data for OS extrapolation.

Purpose of the Study:

  • To assess the utilization of external evidence for overall survival (OS) estimation in oncology STAs by NICE.
  • To identify the sources, types, and application of external data in OS extrapolation.
  • To evaluate the acceptance of external data by NICE's evidence review group (ERG) and appraisal committee.

Main Methods:

  • Systematic review of oncology STAs appraised by NICE between January 2021 and March 2023.
  • Extraction of data on OS extrapolation methods, use of external data, data sources, and acceptance by NICE.
  • Analysis of the frequency and rationale for using external data in different modeling approaches.

Main Results:

  • 32 out of 82 eligible STAs (39%) utilized external data for OS extrapolation.
  • Commonly used external data included trial data (44%), real-world data (47%), and clinical opinion (25%).
  • External data informed post-event transitions, cure assumptions, long-term survival, and surrogacy analyses; NICE accepted external data in 50% of cases.

Conclusions:

  • External data, including real-world data and clinical opinions, are frequently incorporated into oncology STAs for OS estimation.
  • NICE's ERGs and appraisal committees are generally accepting of external data use.
  • Sensitivity analyses and clear justification for data and methods are crucial for the acceptance of external evidence in oncology appraisals.