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

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

490
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
490
Bias01:22

Bias

4.8K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
4.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

75
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
75
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

112
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
112
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

226
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...
226
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
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...
6.3K

You might also read

Related Articles

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

Sort by
Same author

Effects of non-surgical interventions on standing posture and pain in children with flexible flatfoot: a network meta-analysis of randomized controlled trials.

Physical therapy·2026
Same author

Gastric Neoplasm Risk with DPP-4 Inhibitors, GLP-1 Receptor Agonists, and SGLT2 Inhibitors: Network Meta-Analysis of Randomized Trials.

International journal of molecular sciences·2026
Same author

Effects of timing and eating duration of time restricted eating on metabolic outcomes: systematic review and network meta-analysis.

BMJ medicine·2026
Same author

Comparative effects of early physical interventions on preventing intensive care unit-acquired weakness: a systematic review and component network meta-analysis.

BMJ evidence-based medicine·2025
Same author

Agreement Between Different Types of Blood Pressure Monitoring : A Systematic Review and Network Meta-analysis.

Annals of internal medicine·2025
Same author

Trajectory of efficacy and safety across ulotaront dose levels in schizophrenia: a systematic review and dose-response meta-analysis.

The international journal of neuropsychopharmacology·2025
Same journal

Doubly robust augmented weighting estimators for the analysis of externally controlled single-arm trials and unanchored indirect treatment comparisons.

Research synthesis methods·2026
Same journal

Prompt engineering of large language models for paper screening in medical meta-analyses and systematic reviews: A prospective comparative study - CORRIGENDUM.

Research synthesis methods·2026
Same journal

Evaluating the accuracy and speed of eight deduplication tools: A comparative study.

Research synthesis methods·2026
Same journal

A comparison of preprint search aggregators: comprehensive identification of preprints in the information retrieval stage of evidence syntheses.

Research synthesis methods·2026
Same journal

Meta-research on key metrics of preregistered scoping reviews.

Research synthesis methods·2026
Same journal

Facilitators and barriers to engaging patient partners in knowledge syntheses: A stage-based approach.

Research synthesis methods·2026
See all related articles

Related Experiment Video

Updated: Aug 17, 2025

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.2K

Bias propagation in network meta-analysis models.

Hua Li1, Ming-Chieh Shih1,2, Cheng-Jie Song1

  • 1Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.

Research Synthesis Methods
|December 12, 2022
PubMed
Summary
This summary is machine-generated.

Biased evidence in network meta-analysis can impact multiple treatment comparisons. Network structure influences how bias spreads, with well-connected networks potentially diluting bias effects.

Keywords:
Bayesian methodsarm-based modelcontrast-based modelnetwork meta-analysis

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Related Experiment Videos

Last Updated: Aug 17, 2025

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke
09:50

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

Published on: February 12, 2015

11.2K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Area of Science:

  • Biostatistics
  • Medical Research Methodology

Background:

  • Network meta-analysis (NMA) integrates direct and indirect evidence for multi-treatment comparisons.
  • Bias in direct evidence can propagate to other treatment contrasts within an NMA network.

Purpose of the Study:

  • To investigate how biased evidence spreads across a network meta-analysis.
  • To determine the influence of network structure on bias propagation.
  • To compare bias propagation across different statistical models (Lu & Ades, baseline, arm-based).

Main Methods:

  • Extensive simulations under various scenarios.
  • Exploration of bias location, network geometry, and statistical model effects.
  • Application to a real-world network meta-analysis.

Main Results:

  • Network structure significantly impacts bias spread, particularly in the Lu & Ades model.
  • Bias impact is more diluted in well-connected networks due to integration with unbiased evidence.
  • Demonstrated practical application for explaining anomalous NMA results.

Conclusions:

  • Network geometry is a critical factor in understanding bias propagation in NMA.
  • Understanding bias spread can improve the reliability and interpretation of NMA findings.
  • The study provides insights for identifying and mitigating bias in complex treatment comparisons.