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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

370
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
370
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

315
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of...
315
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

99
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.
99
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

403
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
403
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

185
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...
185

You might also read

Related Articles

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

Sort by
Same author

Two-stage sampling for better survival model performance.

BMC medical research methodology·2025
Same author

Adjusted predictions for generalized estimating equations.

Biometrics·2025
Same author

Endoplasmic reticulum protein 5 attenuates platelet endoplasmic reticulum stress and secretion in a mouse model.

Blood advances·2022
Same author

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data.

GigaScience·2022
Same author

Cross-Platform Omics Prediction procedure: a statistical machine learning framework for wider implementation of precision medicine.

NPJ digital medicine·2022
Same journal

STED: flexible cross-modal topic modeling infers cell-type-specific regulatory landscapes from bulk epigenomics.

Briefings in bioinformatics·2026
Same journal

A knowledge-guided deep learning framework for quantitative nucleic acid testing.

Briefings in bioinformatics·2026
Same journal

Optimal transport for label transfer in single-cell multi-omics integration.

Briefings in bioinformatics·2026
Same journal

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same journal

Evaluating completeness, coherence, and consistency of genome-scale function annotations.

Briefings in bioinformatics·2026
Same journal

Transformers for single-cell RNA sequencing: a survey.

Briefings in bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Jun 10, 2025

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

Robust variable selection methods with Cox model-a selective practical benchmark study.

Yunwei Zhang1,2,3, Samuel Muller2,3

  • 1School of Mathematics, Statistics, Chemistry and Physics, Murdoch University, 90 South St, Murdoch WA 6150, Australia.

Briefings in Bioinformatics
|October 14, 2024
PubMed
Summary
This summary is machine-generated.

Robust Cox models outperform non-robust methods for variable selection with high-dimensional omics and survival data. They offer superior performance with outliers, maintaining accuracy and efficiency in their absence.

Keywords:
Cox modelpenalised Cox modelrobust variable selectionsurvival analysisvariable selection

More Related Videos

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
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

Related Experiment Videos

Last Updated: Jun 10, 2025

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
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K

Area of Science:

  • Biostatistics
  • Bioinformatics
  • Genomics

Background:

  • High-dimensional omics data with censored survival information presents variable selection challenges.
  • Skewed survival time distributions necessitate robust statistical methods.
  • Extending robust methods to survival models is an active research area.

Purpose of the Study:

  • To compare the variable selection performance of robust and non-robust penalized Cox models.
  • To evaluate the impact of outliers on variable selection in survival analysis.
  • To provide practical recommendations for variable selection in omics data.

Main Methods:

  • Selective review and empirical comparison of twelve robust and non-robust penalized Cox models.
  • Analysis of high-dimensional omics data with censored survival outcomes.
  • Assessment of variable selection performance under varying conditions, including the presence of outliers.

Main Results:

  • Robust Cox models demonstrate superior variable selection performance compared to non-robust models, especially in the presence of outliers.
  • Subtle variations in covariates and modeling approaches significantly impact method performance.
  • Robust methods maintain good efficiency and accuracy even when outliers are absent.

Conclusions:

  • Robust Cox models are recommended for practical variable selection with high-dimensional omics and censored survival data.
  • These models offer a reliable approach to handling outliers, enhancing the accuracy of variable selection.
  • The study highlights the importance of considering robust methods for improved insights into complex biological data.