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

Censoring Survival Data01:09

Censoring Survival Data

301
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
301
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

368
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...
368
Survival Tree01:19

Survival Tree

197
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...
197
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

225
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.
225
Correlation of Experimental Data01:23

Correlation of Experimental Data

355
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
355
Coefficient of Correlation01:12

Coefficient of Correlation

7.0K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
7.0K

You might also read

Related Articles

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

Sort by
Same author

Joint Frailty Mixture Cure Model for Recurrent Event Data With Dependent Censoring: An MCEM Approach.

Statistics in medicine·2026
Same author

Coinfection of <i>Salmonella</i> Typhi and Hepatitis A in a Patient With Prolonged Fever and Jaundice.

Case reports in infectious diseases·2026
Same author

Prevalence and determinants of common mental health illnesses among reproductive-aged women in Bangladesh: Evidence from Demographic and Health Surveys data 2022.

PLOS mental health·2026
Same author

Change-point detection in Weibull-accelerated failure time models via narrowest significance pursuit.

Journal of biopharmaceutical statistics·2025
Same author

EPIStress: A multimodal dataset of Physiological signals to measure cognitive stress in epilepsy patients.

Scientific data·2025
Same author

Prevalence of extended-spectrum beta-lactamase-producing Enterobacteriaceae isolated from animals in Bangladesh: A systematic review and meta-analysis.

One health (Amsterdam, Netherlands)·2025
Same journal

Predictor-Assisted Nonparametric Graphical Models With Multivariate Error-Prone Data.

Statistics in medicine·2026
Same journal

Optimizing Treatment Decision Estimation for Right-Censored Survival Data Through Parameter Transfer Learning.

Statistics in medicine·2026
Same journal

Latent Class Log-Linear Models for Estimating Diagnostic Test Accuracy Without a Gold Standard: A Simulation Study.

Statistics in medicine·2026
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Nov 1, 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

2.3K

Variable selection for censored data using Modified Correlation Adjusted coRrelation (MCAR) scores.

Afsana Mimi1, Md Hasinur Rahaman Khan1

  • 1Applied Statistics, Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh.

Statistics in Medicine
|June 22, 2021
PubMed
Summary
This summary is machine-generated.

The Modified Correlation Adjusted coRrelation (MCAR) scores method offers efficient variable selection for high-dimensional censored data. It outperforms existing methods, especially when predictors are correlated, and shows superior predictive performance.

Keywords:
AFT modelMCAR scorecensoringhigh-dimensional data

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

10.5K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.7K

Related Experiment Videos

Last Updated: Nov 1, 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

2.3K
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

10.5K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.7K

Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • High-dimensional censored data presents significant analytical challenges due to complex data structures.
  • Variable selection is crucial for building accurate models with such data.
  • Existing methods may struggle with correlated predictors and computational efficiency.

Purpose of the Study:

  • To develop a novel variable selection procedure for high-dimensional censored data using AFT models.
  • To introduce the Modified Correlation Adjusted coRrelation (MCAR) scores method.
  • To evaluate the performance of MCAR against established techniques.

Main Methods:

  • The study proposes the Modified Correlation Adjusted coRrelation (MCAR) scores method, an extension of the CAR scores method.
  • MCAR integrates sample and threshold estimators of the correlation matrix.
  • It employs a greedy approach for simultaneous estimation and variable selection.

Main Results:

  • The MCAR method demonstrates superior performance in variable selection for censored high-dimensional data, particularly when covariates are correlated.
  • It outperforms LASSO, Elastic Net, Boosting, and censored CAR methods.
  • MCAR also exhibits the best predictive performance in empirical studies.

Conclusions:

  • The MCAR scores method provides a computationally efficient and effective approach for variable selection in high-dimensional censored data.
  • It is particularly advantageous in the presence of correlated predictors.
  • MCAR offers a robust alternative for complex statistical modeling and prediction.