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

Correlation and Regression00:53

Correlation and Regression

1.3K
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...
1.3K
Multiple Regression01:25

Multiple Regression

3.0K
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...
3.0K
Regression Analysis01:11

Regression Analysis

5.7K
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:
5.7K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Coefficient of Correlation01:12

Coefficient of Correlation

6.2K
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...
6.2K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

6.0K
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. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
6.0K

You might also read

Related Articles

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

Sort by
Same author

Economic evaluation of adjuvant chemotherapy for non-metastatic sarcoma using the real-world data from the French nationwide DEEPSARC study.

Journal of medical economics·2026
Same author

Overall survival of patients with localized soft tissue sarcoma treated with or without adjuvant chemotherapy (DEEPSARC): a real world nationwide retrospective cohort study in France.

EClinicalMedicine·2026
Same author

Left Ventricular Function and Atrial Remodeling Shape Outcomes After Mitral-Transcatheter Edge-to-Edge Repair.

ESC heart failure·2026
Same author

Linking the NETSARC+ National Sarcoma Database With the SNDS to Evaluate Adjuvant and/or Neoadjuvant Therapy: Report on the Linkage Process and Result (Health Data Hub's DEEPSARC Pilot Project).

Fundamental & clinical pharmacology·2025
Same author

Three-year of hepatocellular carcinoma surveillance in patients with cirrhosis diagnosed between 2009 and 2013: a cohort study based on the French National Health Data System (SNDS) claims data.

Frontiers in oncology·2025
Same author

Interleukin-17 Inhibitors and Early Major Adverse Cardiovascular Events.

JAMA dermatology·2025
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
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Jul 10, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.4K

Cox regression with linked data.

Thanh Huan Vo1,2, Valérie Garès1, Li-Chun Zhang3,4

  • 1Univ Rennes, INSA, CNRS, IRMAR-UMR 6625, Rennes, France.

Statistics in Medicine
|November 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to reduce bias in Cox regression analysis caused by record linkage errors in medical studies. The proposed technique improves the accuracy of estimates when combining data from multiple sources.

Keywords:
Cox regressionadjusted estimating equationlinkage errorsecondary analysisvariance estimation

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

10.2K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.2K

Related Experiment Videos

Last Updated: Jul 10, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.4K
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

10.2K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.2K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Data Science

Background:

  • Record linkage is crucial for integrating data from disparate sources in medical research.
  • Linkage errors are inherent and can introduce bias into statistical analyses, particularly in Cox regression models.
  • Existing methods for addressing linkage errors primarily focus on generalized linear models, leaving a gap for Cox regression.

Purpose of the Study:

  • To develop an adjusted estimating equation for Cox regression analysis using linked data.
  • To address bias stemming from linkage errors when data is prepared by a third party without access to matching variables.
  • To provide an asymptotically unbiased variance estimator for the adjusted Cox regression coefficients.

Main Methods:

  • Proposed an adjusted estimating equation for secondary Cox regression analysis.
  • Conducted Monte Carlo simulations to evaluate the performance of the proposed method.
  • Applied the method to a real-world linked database from the Brest stroke registry.

Main Results:

  • The proposed method significantly reduces bias in Cox model parameter estimation caused by false links.
  • The adjusted estimators demonstrate improved accuracy compared to unadjusted methods.
  • An asymptotically unbiased variance estimator was successfully developed and validated.

Conclusions:

  • The developed method offers a robust solution for handling linkage errors in Cox regression analysis.
  • This approach enhances the reliability of findings from linked medical databases.
  • The study provides a valuable tool for researchers in clinical and epidemiological studies utilizing linked data.