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

Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

1.0K
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates...
1.0K
Ranks01:02

Ranks

285
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
285
Wilcoxon Rank-Sum Test01:21

Wilcoxon Rank-Sum Test

331
The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:
331
Relative Risk01:12

Relative Risk

333
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
333
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

293
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
293
Coefficient of Correlation01:12

Coefficient of Correlation

6.4K
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...
6.4K

You might also read

Related Articles

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

Sort by
Same author

Physicians and artificial intelligence diverge in evaluating large language models on real clinical cases.

NPJ digital medicine·2026
Same author

Kernel-based Maximum likelihood reconstruction of attenuation and activity (MLAA) in SPECT imaging for improved attenuation correction and activity quantification: Simulation, phantom and patient validation studies.

Physics in medicine and biology·2026
Same author

Reply: Radial Wall Strain and the Stepwise Integration of Physiology and Vulnerability in Revascularization Decision Making.

JACC. Asia·2026
Same author

Response to Letter Regarding Article, "Interpreting AI noise Driven Radiation Reduction in Coronary Angiography: The Role of Technical and Clinical Determinants".

Circulation. Cardiovascular interventions·2026
Same author

Comparison of systolic and diastolic CT-FFR for myocardial ischemia diagnosis.

BMC medical imaging·2026
Same author

Dynamic Changes and Prognostic Value of Angio-derived Index of Microcirculatory Resistance in Acute Myocardial Infarction: A Cohort Study.

The Canadian journal of cardiology·2026
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
See all related articles

Related Experiment Video

Updated: Sep 8, 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

2.2K

On correlation rank screening for ultra-high dimensional competing risks data.

Xiaolin Chen1, Chenguang Li1, Tao Zhang2

  • 1School of Statistics, Qufu Normal University, Qufu, People's Republic of China.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new feature screening method for competing risks survival data. The model-free approach effectively identifies relevant features in ultra-high dimensions, outperforming existing methods.

Keywords:
Consistency in rankingfeature screeningmodel-freesure independence screeningultra-high dimensional competing risks 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

10.3K
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

372

Related Experiment Videos

Last Updated: Sep 8, 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

2.2K
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
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

372

Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • Existing feature screening methods are limited to single-event survival data.
  • Competing risks, involving multiple mutually exclusive failure types, lack adequate screening techniques.
  • Ultra-high dimensional data presents unique challenges for traditional statistical analysis.

Purpose of the Study:

  • To develop a novel marginal feature screening method for ultra-high dimensional time-to-event data with competing risks.
  • To provide a robust and model-free approach that is invariant to transformations of event times.
  • To address the limitations of current methods in handling complex failure scenarios.

Main Methods:

  • A new marginal feature screening procedure is proposed for competing risks.
  • The method is designed to be model-free, robust to heavy-tailed distributions and outliers.
  • It demonstrates invariance to monotone transformations of the event time of interest.

Main Results:

  • The proposed method exhibits ranking consistency and sure independence screening properties under mild assumptions.
  • Numerical studies confirm its effectiveness in finite-sample performance.
  • The method was compared favorably against a competitor in simulations.

Conclusions:

  • The developed feature screening method offers a robust solution for ultra-high dimensional competing risks data.
  • It enhances the ability to identify significant features in complex survival analysis scenarios.
  • The approach provides a valuable tool for analyzing time-to-event data with multiple failure types.