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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

5.0K
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...
5.0K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

8.1K
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...
8.1K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

4.2K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
4.2K
Regression Toward the Mean01:52

Regression Toward the Mean

7.3K
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...
7.3K
Outliers and Influential Points01:08

Outliers and Influential Points

6.8K
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...
6.8K
What Are Outliers?01:12

What Are Outliers?

5.7K
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...
5.7K

You might also read

Related Articles

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

Sort by
Same author

Statistics and AI - A Fireside Conversation.

Harvard data science review·2026
Same author

Unobserved heterogeneity in threshold regression based on the hitting times of a reflected Brownian motion for recurrent hypoglycemia.

Lifetime data analysis·2026
Same author

Robust CATE estimation using novel ensemble methods.

Journal of biopharmaceutical statistics·2026
Same author

Learning Optimal Dynamic Treatment Regimens Subject to Stagewise Risk Controls.

Journal of machine learning research : JMLR·2025
Same author

Group Sequential Trial Design Using Stepwise Monte Carlo for Increased Flexibility and Robustness.

Statistics in medicine·2025
Same author

Controlling Cumulative Adverse Risk in Learning Optimal Dynamic Treatment Regimens.

Journal of the American Statistical Association·2025
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Apr 15, 2026

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

1.4K

Detecting outlying trials in network meta-analysis.

Jing Zhang1, Haoda Fu2, Bradley P Carlin3

  • 1Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, 20740, MD, U.S.A.

Statistics in Medicine
|April 9, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces novel Bayesian methods for identifying and handling trial-level outliers in network meta-analysis (NMA). These methods improve the accuracy of evidence synthesis by excluding deviating studies, crucial for reliable treatment comparisons.

Keywords:
detection measuresnetwork meta-analysistrial-level outliers

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K
Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

2.0K

Related Experiment Videos

Last Updated: Apr 15, 2026

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

1.4K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K
Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

2.0K

Area of Science:

  • Biostatistics
  • Evidence Synthesis
  • Clinical Trial Analysis

Background:

  • Network meta-analysis (NMA) synthesizes evidence from multiple treatment comparisons.
  • Identifying and managing trial-level outliers is critical for unbiased NMA.
  • Existing methods for NMA primarily address heterogeneity and inconsistency, not outliers.

Purpose of the Study:

  • To propose and evaluate Bayesian methods for detecting trial-level outliers in NMA.
  • To assess the impact of outlier inclusion on NMA estimation bias.
  • To provide practical tools for robust evidence synthesis in complex treatment comparisons.

Main Methods:

  • Development of several Bayesian outlier detection measures.
  • Application of these measures to a diabetes clinical dataset.
  • Conducting simulation studies in arm-based and contrast-based NMA models.

Main Results:

  • The proposed Bayesian methods effectively identify trial-level outliers.
  • Excluding outliers can reduce bias in NMA estimations.
  • Performance of outlier detection varies between arm-based and contrast-based models.

Conclusions:

  • Bayesian outlier detection offers a valuable approach for enhancing NMA robustness.
  • Proper handling of trial-level outliers is essential for reliable meta-analysis results.
  • Further research can refine these methods for broader application in health research.