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

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

184
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...
184
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

97
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.
97
Actuarial Approach01:20

Actuarial Approach

63
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,...
63
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

152
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...
152
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

100
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,...
100
Cancer Survival Analysis01:21

Cancer Survival Analysis

328
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...
328

You might also read

Related Articles

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

Sort by
Same author

ASO Visual Abstract: Socioeconomic Status and Survival in Sarcomas of the Breast: Nationwide Cohort Study.

Annals of surgical oncology·2026
Same author

Socioeconomic Status and Survival in Sarcomas of the Breast: Nationwide Cohort Study.

Annals of surgical oncology·2026
Same author

Multilevel associations between prostate cancer testing and socioeconomic position: a population-based register study from Stockholm, Sweden.

BMJ public health·2026
Same author

Risk of Transformation to Acute Myeloid Leukaemia and Myelodysplastic Syndromes in Patients With Myeloproliferative Neoplasms Over Attained Age and Time Since Diagnosis: A Nationwide Cohort Study.

European journal of haematology·2026
Same author

Empirical and projected economic burden of chronic myeloid leukaemia in Sweden from 2015 to 2030: A population-based study.

British journal of haematology·2025
Same author

Body height and the excess cancer risk in men.

International journal of cancer·2025
Same journal

Use and Outcomes of Cost-Effectiveness Analyses and Additional Considerations in Pharmaceutical Reimbursement in the Netherlands.

PharmacoEconomics·2026
Same journal

SMART Planning of Early-Stage Health Economic Decision Models for Mechanism-Based Precision Treatments in Rare Cancers: An Application and Tool Update.

PharmacoEconomics·2026
Same journal

Balancing Access and Value in Multi-Indication Medicines: Implications of PD-L1 Broad Listings in Australia.

PharmacoEconomics·2026
Same journal

Applications of Structural Expert Elicitations for Economic Evaluations: A Systematic Review Update.

PharmacoEconomics·2026
Same journal

Community Pharmacist Preferences for Providing a Dose Administration Aid Service in Australia: A Discrete Choice Experiment.

PharmacoEconomics·2026
Same journal

A Critical Systematic Review of Modelling Approaches and Methodologies used in Hyperlipidaemia Economic Evaluations.

PharmacoEconomics·2026
See all related articles

Related Experiment Video

Updated: Jun 6, 2025

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

10.7K

A Multistate Model Incorporating Relative Survival Extrapolation and Mixed Time Scales for Health Technology

Enoch Yi-Tung Chen1, Paul W Dickman2, Mark S Clements2

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, 171 77, Stockholm, Sweden. enoch.yitung.chen@ki.se.

Pharmacoeconomics
|November 25, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new multistate model for health technology assessment that integrates relative survival extrapolation with mixed time scales. This flexible parametric approach improves survival prediction accuracy compared to standard methods.

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

2.0K
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

10.1K

Related Experiment Videos

Last Updated: Jun 6, 2025

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

10.7K
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

2.0K
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

10.1K

Area of Science:

  • Health Technology Assessment
  • Biostatistics
  • Survival Analysis

Background:

  • Multistate models are crucial in health technology assessment but face challenges in precise and unbiased survival extrapolation.
  • Existing methods struggle with integrating diverse data sources for long-term survival predictions.

Purpose of the Study:

  • To develop an individual-level, continuous-time multistate model.
  • To integrate relative survival extrapolation with mixed time scales for improved accuracy.
  • To address limitations in current survival extrapolation techniques within multistate models.

Main Methods:

  • Utilized an illness-death model for illustration.
  • Employed flexible parametric models to estimate transition rates.
  • Updated R packages (hesim, microsimulation) for simulating event times with mixed time scales.
  • Compared standard vs. flexible parametric models and all-cause vs. relative survival frameworks.

Main Results:

  • The proposed model successfully enables relative survival extrapolation within a multistate framework.
  • The flexible parametric approach demonstrated better agreement with observed data in the case study.
  • Outperformed commonly used standard parametric models within an all-cause survival framework.

Conclusions:

  • Introduced a novel multistate model combining flexible parametric modeling, relative survival extrapolation, and mixed time scales.
  • Offers a viable alternative for integrating short-term clinical trial data with long-term external data in health technology assessment.
  • Enhances the precision and reduces bias in survival extrapolation for health technology assessment.