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

Outliers and Influential Points01:08

Outliers and Influential Points

5.6K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
5.6K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.4K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.4K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.7K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.7K
What Are Outliers?01:12

What Are Outliers?

4.8K
Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
4.8K
Regression Toward the Mean01:52

Regression Toward the Mean

6.7K
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...
6.7K
Significance Testing: Overview01:04

Significance Testing: Overview

9.9K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
9.9K

You might also read

Related Articles

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

Sort by
Same author

Continuous glucose monitoring-derived time in range is associated with changes in arterial stiffness in type 2 diabetes.

The Journal of clinical endocrinology and metabolism·2026
Same author

Heart failure and renal outcomes with angiotensin receptor blockers compared with calcium channel blockers in patients with chronic kidney disease: a target trial emulation.

Heart (British Cardiac Society)·2026
Same author

A Nomogram Predicts the Need for Internal Iliac Vein Dissection During Renal Transplantation: A Multicenter Collaborative Study.

Transplantation proceedings·2026
Same author

Risk of urinary tract infection with SGLT2 inhibitor initiation in patients with immune-mediated inflammatory diseases and type 2 diabetes: A target trial emulation using a Japanese hospital-based claims database.

Journal of diabetes investigation·2026
Same author

Influence Analyses of "Designs" for Evaluating Inconsistency in Network Meta-Analysis.

Statistics in medicine·2026
Same author

Smartphone CBT engagement and depressive symptoms: secondary analysis of the RESiLIENT trial using a time-varying exposure approach.

Psychological medicine·2026

Related Experiment Video

Updated: Dec 9, 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

11.5K

Outlier detection and influence diagnostics in network meta-analysis.

Hisashi Noma1, Masahiko Gosho2, Ryota Ishii3

  • 1Department of Data Science, The Institute of Statistical Mathematics, Tokyo, Japan.

Research Synthesis Methods
|September 13, 2020
PubMed
Summary

This study introduces new methods to identify influential studies in network meta-analysis, crucial for accurate treatment comparisons. Detecting and removing outlying data prevents biased results and ensures reliable evidence synthesis.

Keywords:
contrast-based modelinfluence diagnosticsmultivariate meta-analysisnetwork meta-analysisoutlier detection

More Related Videos

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

3.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.2K

Related Experiment Videos

Last Updated: Dec 9, 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

11.5K
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

3.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.2K

Area of Science:

  • Biostatistics
  • Evidence Synthesis
  • Pharmacological Research

Background:

  • Network meta-analysis (NMA) synthesizes multiple treatments but can be skewed by influential outlier studies.
  • Identifying and addressing these outliers is critical for unbiased and reliable NMA results.

Purpose of the Study:

  • To propose novel frequentist methods for detecting outlying and influential studies within NMA.
  • To enhance the accuracy and trustworthiness of evidence synthesis in comparative effectiveness research.

Main Methods:

  • Developed four influence measures for NMA, including leave-one-trial-out cross-validation (comparison-specific studentized residual, relative change measures for covariance and heterogeneity matrices).
  • Proposed a model-based approach using a likelihood ratio statistic with a mean-shifted outlier detection model.
  • Applied methods to an NMA of antihypertensive drugs, including models with missing outcomes and adjusted degrees of freedom.

Main Results:

  • Successfully identified three influential trials in the antihypertensive drug NMA, including one retracted due to data falsification.
  • Demonstrated that omitting these influential studies significantly altered comparative efficacy estimates and drug rankings.
  • Validated the effectiveness of the proposed detection methods in a real-world NMA scenario.

Conclusions:

  • The proposed frequentist methods effectively detect outlying and influential studies in network meta-analysis.
  • Exclusion of identified influential studies can substantially change NMA outcomes, highlighting the importance of outlier detection.
  • These methods improve the reliability of evidence synthesis for clinical decision-making.