Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

Sign Test for Matched Pairs

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...

Confounding in Epidemiological Studies

Confounding in Epidemiological Studies

Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This phenomenon...

Multiple Comparison Tests

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

Wilcoxon Signed-Ranks Test for Matched Pairs

Wilcoxon Signed-Ranks Test for Matched Pairs

The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...

Factors Influencing Attraction III: Similarity

Factors Influencing Attraction III: Similarity

The similarity hypothesis suggests that individuals are more likely to form relationships with others who share similar attitudes, beliefs, values, and interests. This concept has been widely studied in social psychology, demonstrating that perceived similarity fosters interpersonal attraction. In an experiment supporting this hypothesis, participants were presented with fabricated information indicating that strangers held attitudes similar to their own. The results showed that participants...

You might also read

Related Articles

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

Sort by

Same author

Risk, Predictors, and Outcomes of Acute Kidney Injury in Patients Admitted to Intensive Care Units in Egypt.

Scientific reports·2017

Same author

Long-Term Progression of Coronary Artery Calcification Is Independent of Classical Risk Factors, C-Reactive Protein, and Parathyroid Hormone in Renal Transplant Patients.

Cardiorenal medicine·2017

Same author

Obesity and kidney disease: Hidden consequences of the epidemic.

African journal of primary health care & family medicine·2017

Same author

Validation of echocardiographic criteria for the clinical diagnosis of heart failure in chronic kidney disease.

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association·2017

Same author

Optimizing hypertension management in renal transplantation: a call to action.

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association·2017

Same author

Optimizing hypertension management in renal transplantation: a call to action.

Journal of hypertension·2017

Search research articles

Related Experiment Video

Updated: Jun 4, 2026

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

Matching, an appealing method to avoid confounding?

Michiel A de Graaf¹, Kitty J Jager, Carmine Zoccali

¹Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands. M.A.de_Graaf @ LUMC.nl

Nephron. Clinical Practice

|February 5, 2011

Summary

This summary is machine-generated.

Matching in case-control studies helps control confounding but can reduce effect estimates. Special statistical analysis is needed, especially for hard-to-measure confounders.

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Related Experiment Videos

Last Updated: Jun 4, 2026

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

Epidemiology
Biostatistics

Background:

Matching is a key technique in study design to mitigate confounding.
In cohort studies, matching ensures equal distribution of confounding variables between exposed and unexposed groups.
Matched case-control studies pair individuals with a disease (cases) to those without (controls).

Purpose of the Study:

To explain the impact of matching on effect estimates in case-control studies.
To highlight the need for statistical adjustments in matched case-control designs.
To identify situations where matched case-control studies are particularly advantageous.

Main Methods:

Describes the principle of matching in cohort studies.
Explains the matching process in case-control studies.
Discusses the consequence of matching on exposure and confounder distribution.

Main Results:

Matching can lead to an "overmatching" effect, making case and control groups too similar.
This similarity results in attenuated effect estimates (odds ratio closer to 1).
Matched case-control studies necessitate specific statistical analyses to correct for this bias.

Conclusions:

Matched case-control studies require statistical correction due to potential attenuation of effect estimates.
This design is particularly valuable when dealing with confounders that are difficult to measure accurately.
Careful consideration of matching's impact on statistical analysis is crucial for valid results.