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

Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

172
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...
172
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

294
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...
294
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

322
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
322
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

331
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
331
Ladder Diagrams: Complexation Equilibria01:07

Ladder Diagrams: Complexation Equilibria

438
Ladder diagrams are useful for evaluating equilibria involving metal-ligand complexes. The vertical scale of the ladder diagram represents the concentration of unreacted or free ligand, pL. The horizontal lines on the scale depict the log of stepwise formation constants for metal-ligand complexes and indicate the dominant species in all the regions.
The formation constant, K1, for the formation of Cd(NH3)2+ complex from cadmium and ammonia is 3.55 × 102. Log K1 (i.e. pNH3) is 2.55, and...
438
Relationship Formation02:12

Relationship Formation

41.7K
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,...
41.7K

You might also read

Related Articles

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

Sort by
Same author

Tool for Converting ADHD Rating Scales Scores Based on Individual Participant Data from 53 Randomized Controlled Trials of ADHD Medications.

Journal of child and adolescent psychopharmacology·2026
Same author

Dental caries and periodontal status in children and adolescents with type 1 diabetes mellitus: an umbrella review.

European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry·2026
Same author

Comparison of overall survival across treatment modalities for oesophageal, gastroesophageal, and gastric cancer: protocol for a systematic review and network meta-analysis.

Systematic reviews·2026
Same author

Response to: Matters Arising in relation to "Evaluating agreement between individual nutrition randomised controlled trials and cohort studies - a meta-epidemiological study".

BMC medicine·2026
Same author

Is it efficacy or just improvement? A re-evaluation of the meta-analysis presented by Zhang et al.

Journal of affective disorders·2026
Same author

Comparative efficacy and tolerability of antidopaminergic and muscarinic antipsychotics for acute schizophrenia: a network meta-analysis of randomised controlled trials indexed in international English and Chinese databases.

Lancet (London, England)·2026

Related Experiment Video

Updated: Oct 3, 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

11.3K

Answering complex hierarchy questions in network meta-analysis.

Theodoros Papakonstantinou1,2, Georgia Salanti2, Dimitris Mavridis3,4

  • 1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Centre-University of Freiburg, Freiburg, Germany.

BMC Medical Research Methodology
|February 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for creating treatment hierarchies in network meta-analysis, linking treatment rankings to specific clinical questions. This approach enhances decision-making by providing clearer, more relevant treatment comparisons.

Keywords:
Clinically relevant questionEvidence synthesisIndirect evidenceProbabilistic ranking

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

1.2K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

771

Related Experiment Videos

Last Updated: Oct 3, 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

11.3K
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

1.2K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

771

Area of Science:

  • Biostatistics
  • Health Services Research
  • Clinical Epidemiology

Background:

  • Network meta-analysis (NMA) can generate treatment hierarchies but often lacks clinical relevance.
  • Existing hierarchies in NMA are rarely tied to specific, practical decision-making questions.

Purpose of the Study:

  • To define treatment hierarchy and ranking within NMA.
  • To develop a simulation method for estimating hierarchy probabilities.
  • To propose a stepwise approach for linking clinical questions to hierarchy analysis.

Main Methods:

  • Defined treatment hierarchy and ranking in NMA.
  • Developed a simulation method to estimate the probability of each hierarchy.
  • Proposed a stepwise approach to translate clinical questions into hierarchy questions.
  • Quantified uncertainty in decision criteria.

Main Results:

  • Demonstrated method with two networks: COPD treatments (most probable hierarchy 28%) and antidepressants (Vortioxetine, Bupropion, Escitalopram ranked top 3 with 19% probability).
  • Showcased how hierarchy frequencies relate to common ranking metrics.

Conclusions:

  • The developed method provides a generalized approach for NMA treatment hierarchies.
  • This approach links treatment ranking to clear, clinically relevant decision questions.
  • Enhances the utility of NMA for clinical decision support.