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

Regression Toward the Mean01:52

Regression Toward the Mean

6.9K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.9K
Censoring Survival Data01:09

Censoring Survival Data

539
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...
539
Multiple Regression01:25

Multiple Regression

3.8K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.8K
Correlation and Regression00:53

Correlation and Regression

3.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
3.2K
Regression Analysis01:11

Regression Analysis

8.1K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
8.1K
Microsoft Excel: Regression Analysis01:18

Microsoft Excel: Regression Analysis

1.5K
Regression analysis in Microsoft Excel is a powerful statistical method for examining the relationship between a dependent variable and one or more independent variables. It's used extensively in fields such as economics, biology, and business to predict outcomes, understand relationships, and make data-driven decisions. The most common type is linear regression, which attempts to fit a straight line through the data points to model the relationship between variables.
To perform regression...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Variance Estimation for Weighted Average Treatment Effects.

Statistics in biosciences·2026
Same author

Estimation and inference of the win ratio for two hierarchical endpoints subject to censoring and missing data.

Journal of biopharmaceutical statistics·2026
Same author

Exploring the Unmet Need in Acute Ischemic Stroke Patients Not Treated With Intravenous Alteplase: The Get With The Guidelines-Stroke Registry.

Stroke (Hoboken, N.J.)·2026
Same author

A Tutorial for Propensity Score Weighting Methods Under Violations of the Positivity Assumption.

Statistics in medicine·2025
Same author

Association of Social Media Recruitment and Depression Among Racially and Ethnically Diverse Metabolic and Bariatric Surgery Candidates: Prospective Cohort Study.

JMIR formative research·2025
Same author

Development of a natural language processing algorithm to extract social determinants of health from clinician notes.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons·2025

Related Experiment Video

Updated: Jan 27, 2026

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

10.8K

Cox regression model with randomly censored covariates.

Folefac D Atem1, Roland A Matsouaka2,3, Vincent E Zimmern4,5

  • 1Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, TX, USA.

Biometrical Journal. Biometrische Zeitschrift
|March 26, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to handle randomly right-censored covariates in Cox regression models. The novel approach improves statistical efficiency and reduces bias compared to traditional complete-case analysis.

Keywords:
Cox proportional hazards modelcensored covariatecomplete-case analysisrandom censoringsurvival analysis

More Related Videos

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

8.4K
Imaging Neurons within Thick Brain Sections Using the Golgi-Cox Method
10:26

Imaging Neurons within Thick Brain Sections Using the Golgi-Cox Method

Published on: April 18, 2017

19.2K

Related Experiment Videos

Last Updated: Jan 27, 2026

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

10.8K
Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

8.4K
Imaging Neurons within Thick Brain Sections Using the Golgi-Cox Method
10:26

Imaging Neurons within Thick Brain Sections Using the Golgi-Cox Method

Published on: April 18, 2017

19.2K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Cox proportional hazards models are widely used for time-to-event data.
  • Methods for censored outcomes are common, but methods for censored covariates are less developed.
  • Complete-case analysis (CCA) is an inefficient method for randomly censored covariates, potentially leading to biased results.

Purpose of the Study:

  • To develop a novel statistical method for analyzing time-to-event data with randomly right-censored covariates.
  • To address the limitations of existing methods like CCA for censored covariates.
  • To improve the accuracy and efficiency of estimating covariate effects in survival analysis.

Main Methods:

  • Developed a conditional mean imputation method for randomly censored covariates.
  • Utilized Kaplan-Meier estimates and Cox proportional hazards models for imputation.
  • Evaluated the proposed method using simulation studies.

Main Results:

  • The proposed imputation method demonstrates good bias reduction.
  • The method achieves improved statistical efficiency compared to CCA.
  • Simulations confirm the effectiveness of the novel approach for censored covariates.

Conclusions:

  • The novel imputation method provides a robust solution for handling randomly right-censored covariates in Cox regression.
  • This approach offers a more statistically efficient and less biased alternative to CCA.
  • The method is applicable to real-world epidemiological studies, such as the Framingham Heart Study.