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

Regression Toward the Mean

Regression Toward the Mean

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 researchers try to extrapolate results...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...

Friedman Two-way Analysis of Variance by Ranks

Friedman Two-way Analysis of Variance by Ranks

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 from...

Methods of Medium Optimization

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...

Randomized Experiments

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test

In parametric statistics, two fundamental tests stand out for their utility and wide application: the Student's t-test and goodness-of-fit tests. These tests provide researchers with a robust method for drawing insights from data, testing hypotheses, and making informed decisions based on their findings.
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...

You might also read

Related Articles

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

Sort by

Same author

The effect of motivational interviewing on physical activity after hip fracture: a multisite, open-label, randomised controlled trial in Australia.

The lancet. Healthy longevity·2025

Same author

Using Geospatial Analysis to Determine the Proximity of Community Gyms for a Population-Based Cohort of Young People with Cerebral Palsy.

Physiotherapy Canada. Physiotherapie Canada·2025

Same author

A community-based physical activity intervention (<i>FitSkills</i>) can improve participation attendance of young people with disability: a stepped wedge cluster randomised trial.

British journal of sports medicine·2025

Same author

Surgical site infections complicating coronary artery bypass graft surgery in Australia: time trends in infection rates, surgical antimicrobial prophylaxis, and pathogens using a comprehensive surveillance network, 2010-2023.

The Journal of hospital infection·2025

Same author

Healthcare-associated infections in long-term care facilities: a systematic review and meta-analysis of point prevalence studies.

BMJ public health·2025

Same author

Antimicrobial-resistant central line-associated bloodstream infections in adult intensive care units: findings from an Australian surveillance network, 2011-2022.

Infection control and hospital epidemiology·2024

Search research articles

Related Experiment Video

Updated: May 17, 2026

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

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

Published on: July 3, 2020

Transforming the Model T: random effects meta-analysis with stable weights.

Michael J Malloy¹, Luke A Prendergast, Robert G Staudte

¹Department of Mathematics and Statistics, La Trobe University, Melbourne, Victoria 3086, Australia.

Statistics in Medicine

|October 26, 2012

Summary

This summary is machine-generated.

This study introduces a variance stabilizing transformation to improve meta-analysis for skewed data and estimated variances. The new methods enhance parameter estimation and confidence intervals for fixed or random effects models.

More Related Videos

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Related Experiment Videos

Last Updated: May 17, 2026

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

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

Published on: July 3, 2020

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials

Published on: April 19, 2024

Area of Science:

Statistical methodology
Biostatistics
Meta-analysis

Background:

Standard meta-analysis assumes normal distributions and known variances, which often doesn't reflect real-world data.
Existing methods struggle with skewed effect size distributions and estimated variances.

Purpose of the Study:

To develop improved meta-analytic methods for skewed distributions and estimated variances.
To enhance parameter estimation and confidence interval coverage in meta-analysis.

Main Methods:

Application of variance stabilizing transformations, particularly for Student t-distributions.
Utilizing stable weights and profile approximate likelihood intervals post-stabilization.
Incorporating finite sample bias correction for improved coverage.

Main Results:

Stabilization significantly improves parameter estimation and confidence intervals for fixed and random effects models.
Finite sample bias correction further enhances coverage accuracy.
A simple t-interval effectively covers the overall effect size without needing inter-study variance estimation.

Conclusions:

The proposed methods offer a robust alternative to traditional meta-analytic techniques when dealing with non-normal data.
The methodology is applicable to various study outcomes with available variance stabilizers.
The findings were validated through simulations and real-world medical meta-analyses.