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

Mismatch Repair

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...

Mismatch Repair

Mismatch Repair

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...

Relative Risk

Relative Risk

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...

Relationship Formation

Relationship Formation

What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...

Reliability and Validity

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.

You might also read

Related Articles

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

Sort by

Same author

Comparing Area-level Patient Density and Physician Prescribing Preference Instruments for the Effect of Antidiabetics on Adverse Cardiovascular Events Among Medicare Beneficiaries.

Epidemiology (Cambridge, Mass.)·2025

Same author

Geospatial distribution of the adoption of dipeptidyl-peptidase-4 inhibitors for type 2 diabetes among Medicare beneficiaries.

Spatial and spatio-temporal epidemiology·2025

Same author

Myocarditis in Patients Starting Combination Checkpoint Inhibitor Therapy: Analysis of a Commercial Claims Database.

Journal of the American Heart Association·2024

Same author

Semiparametric Allocation of Subjects to Cohort Strata.

Epidemiology (Cambridge, Mass.)·2023

Same author

Stratified analysis in comparative effectiveness studies that emulate randomized trials.

Pharmacoepidemiology and drug safety·2023

Same author

Antibiotic prescribing patterns among patients admitted to an academic teaching hospital for COVID-19 during the first wave of the pandemic in Toronto: A retrospective, controlled study.

Journal of the Association of Medical Microbiology and Infectious Disease Canada = Journal officiel de l'Association pour la microbiologie medicale et l'infectiologie Canada·2022

Search research articles

Related Experiment Video

Updated: May 9, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Matching on provider is risky.

Alexander M Walker¹

¹World Health Information Science Consultants, Riverside Center, Suite 2-400, 275 Grove Street, Newton, MA 02466, USA. Alec.Walker@WHISCON.com

Journal of Clinical Epidemiology

|July 16, 2013

Summary

This summary is machine-generated.

Matching on provider in observational studies can worsen bias, not eliminate it. Researchers must carefully consider controls to avoid reducing the effect of important confounders.

Keywords:

Amplification bias Comparative effectiveness research Instrumental variables Matching Pseudorandomization Unmeasured confounders Z-bias

More Related Videos

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Related Experiment Videos

Last Updated: May 9, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Biostatistics
Health Services Research
Observational Studies

Background:

Confounding bias is a critical concern in observational studies.
The degree of bias is influenced by the proportion of treatment variation attributable to confounders and instruments.

Purpose of the Study:

To demonstrate how matching on provider can exacerbate, rather than remove, bias.
To illustrate the impact of matching on provider in a constructed example.

Main Methods:

Analysis of confounding bias in observational studies.
Illustrative example of bias introduced by matching on provider for coronary artery bypass graft surgery patients.

Main Results:

Matching on provider removes a "benign" source of treatment variability.
Unmeasured confounders may become the primary determinants of treatment after matching on provider.

Conclusions:

Researchers must clearly articulate the sources of pseudorandom variation in observational studies.
Care should be taken not to diminish the impact of crucial confounders through excessive control measures.