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

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

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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,...

You might also read

Related Articles

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

Sort by
Same author

Gene Expression Profiling of Peripheral Blood and Endometrial Cancer Risk Factors: Systems Epidemiology Approach in the NOWAC Postgenome Cohort Study.

Lifestyle genomics·2026
Same author

Beyond Women's Health: Long-Term Human Papillomavirus-Related Cancer Trends in Norway.

The Journal of infectious diseases·2025
Same author

Impact of Multicohort Human Papillomavirus Vaccination on Cervical Cancer in Women Below 30 Years of Age: Lessons Learned From the Scandinavian Countries.

The Journal of infectious diseases·2024
Same author

Sex differences in early-onset atrial fibrillation in Norwegian primary care: a retrospective national database analysis.

Open heart·2024
Same author

Correction: Whole exome sequencing of high-risk neuroblastoma identifies novel non-synonymous variants.

PloS one·2024
Same author

Regulation of cardiomyocyte t-tubule structure by preload and afterload: Roles in cardiac compensation and decompensation.

The Journal of physiology·2024

Related Experiment Video

Updated: Jun 17, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Survival prediction from clinico-genomic models--a comparative study.

Hege M Bøvelstad1, Ståle Nygård, Ornulf Borgan

  • 1Department of Mathematics, University of Oslo, Blindern, NO 0316 Oslo, Norway. hegembo@math.uio.no

BMC Bioinformatics
|December 17, 2009
PubMed
Summary
This summary is machine-generated.

Combining clinical and genomic data improves survival prediction models. Clinico-genomic models, particularly using ridge regression, outperform models using only genomic data or clinical covariates alone.

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

Related Experiment Videos

Last Updated: Jun 17, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 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

Area of Science:

  • Biostatistics
  • Bioinformatics
  • Genomics

Background:

  • Survival prediction from high-dimensional genomic data is crucial in medical research.
  • Current methods often neglect valuable clinical covariates.
  • Combining clinical and genomic data shows promise but lacks systematic study.

Purpose of the Study:

  • To develop and evaluate clinico-genomic prediction models using Cox regression.
  • To systematically compare prediction performance using clinical data only, genomic data only, and combined data.

Main Methods:

  • Proposed a Cox regression-based clinico-genomic model.
  • Applied dimension reduction techniques to genomic variables.
  • Evaluated seven prediction methods including lasso, ridge regression, and principal components regression.
  • Compared model performance on three survival datasets with clinical and gene expression data.

Main Results:

  • Clinical covariates alone often yield better predictions than genomic data alone.
  • Clinico-genomic models consistently outperform genomic-only models.
  • Ridge regression for dimension reduction in genomic data showed strong performance.

Conclusions:

  • Clinico-genomic models offer superior survival prediction compared to genomic-only or clinical-only models.
  • Ridge regression is an effective method for dimension reduction in clinico-genomic analysis.
  • Integrating clinical and genomic data enhances predictive accuracy in survival analysis.