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

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

180
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.
180
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

288
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
288
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

362
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...
362
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

266
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...
266
Survival Tree01:19

Survival Tree

146
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
146
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

565
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...
565

You might also read

Related Articles

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

Sort by
Same author

Smooth and shape-constrained quantile distributed lag models.

Biometrics·2025
Same author

Heterogeneity-aware integrative regression for ancestry-specific association studies.

Biometrics·2024
Same author

Multiresolution categorical regression for interpretable cell-type annotation.

Biometrics·2023
Same author

Mixed-type multivariate response regression with covariance estimation.

Statistics in medicine·2022
Same author

Scalable algorithms for semiparametric accelerated failure time models in high dimensions.

Statistics in medicine·2022
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Sep 1, 2025

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

10.3K

Dimension reduction for integrative survival analysis.

Aaron J Molstad1, Rohit K Patra2

  • 1Department of Statistics and Genetics Institute, University of Florida, Gainesville, Florida, USA.

Biometrics
|August 14, 2022
PubMed
Summary
This summary is machine-generated.

We developed a new statistical method for analyzing survival data across multiple cancer types. This approach efficiently identifies key protein biomarkers linked to patient survival, improving prediction accuracy.

Keywords:
Cox proportional hazards modeldimension reductionintegrative survival analysismajorize-minimizepenalty methodreduced-rank regressionvariable selection

More Related Videos

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.2K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

186

Related Experiment Videos

Last Updated: Sep 1, 2025

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

10.3K
Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.2K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

186

Area of Science:

  • Biostatistics
  • Genomics
  • Computational Biology

Background:

  • Integrative survival analysis is crucial for understanding disease heterogeneity across populations.
  • High-dimensional data in cancer research presents challenges for traditional survival models.
  • Existing methods may not fully leverage shared information across distinct cancer types.

Purpose of the Study:

  • To develop a dimension reduction technique for integrative survival analysis with high-dimensional predictors.
  • To propose a constrained maximum partial likelihood estimator that borrows information across populations.
  • To identify key factors (linear combinations of predictors) influencing survival in multi-cancer studies.

Main Methods:

  • Utilized a constrained maximum partial likelihood estimator for dimension reduction.
  • Employed "distance-to-set" penalties to estimate linear combinations (factors) of predictors.
  • Imposed low-rankness and sparsity on the regression coefficient matrix.
  • Derived asymptotic results for estimator efficiency.

Main Results:

  • The proposed estimator demonstrated higher efficiency compared to fitting separate Cox proportional hazards models for each population.
  • Numerical experiments showed superior performance against competing methods across various data models.
  • Applied to pan-cancer survival analysis, identifying six factors (from 20 proteins) explaining survival across 18 cancer types.
  • Validated the model's predictive capability on four external datasets, outperforming competitors.

Conclusions:

  • The novel method provides an efficient approach for dimension reduction in integrative survival analysis.
  • The identified protein-based factors offer insights into shared survival mechanisms across diverse cancer types.
  • The method enhances predictive accuracy for survival outcomes in multi-cancer settings.