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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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:
Bias01:22

Bias

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...
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

You might also read

Related Articles

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

Sort by
Same author

Physical and mental health impact of perimenopause, menopause and post menopause in a diverse global population (MARIE Project- Global Chapter WP 2a): cross-sectional quantitative data from a mixed-methods study.

EClinicalMedicine·2026
Same author

Menopause in Brazil: lived experiences, inequities, and opportunities for inclusive care (MARIE-Brazil WP2a).

Women & health·2026
Same author

A perspective on economic barriers and disparities in access to hormone replacement therapy in LMICs (MARIE-WP2b).

Scientific reports·2026
Same author

Pigmented paravenous retinochoroidal atrophy (PPRCA): a systematic review.

International ophthalmology·2026
Same author

Intersecting inequities: a systematic review of socio-cultural, economic, and legal determinants of violence against women and girls in Asia (ANULA project-WP1 Evidence Synthesis).

BMC public health·2026
Same author

Breaking the silence and building strength; rethinking menopause care through exercise and cultural insight.

Frontiers in global women's health·2026

Related Experiment Video

Updated: Jun 2, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

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

Meta-analysis and sensitivity analysis for multi-arm trials with selection bias.

Hathaikan Chootrakool1, Jian Qing Shi, Rongxian Yue

  • 1Suan Dusit Rajabhat University, Thailand.

Statistics in Medicine
|May 4, 2011
PubMed
Summary

This study introduces a new meta-analysis model for multi-arm trials to address selection bias. The method helps ensure more accurate conclusions on treatment effectiveness by accounting for potentially missing studies.

Related Experiment Videos

Last Updated: Jun 2, 2026

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
08:36

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:

  • Biostatistics
  • Clinical Trials Methodology
  • Evidence Synthesis

Background:

  • Multi-arm trials meta-analysis synthesizes evidence from multiple comparisons to assess treatment effectiveness.
  • Publication bias, where significant results are favored, can lead to over-optimistic conclusions in meta-analyses.
  • Selection bias in meta-analysis may result in inaccurate inferences about treatment efficacy.

Purpose of the Study:

  • To define a random-effect meta-analysis model for multi-arm trials that accounts for heterogeneity.
  • To address publication bias using sensitivity analysis and a selection model.
  • To investigate the sensitivity of parameter estimates to varying degrees of selection bias.

Main Methods:

  • Developed a random-effect meta-analysis model for multi-arm trials using normal approximation for empirical log-odds ratios.
  • Employed sensitivity analysis to evaluate the impact of potential missing studies.
  • Defined a selection model to quantify and adjust for different amounts of selection bias.

Main Results:

  • The proposed model allows for heterogeneity among studies in multi-arm meta-analyses.
  • The selection model quantifies the potential impact of publication bias on treatment effectiveness estimates.
  • Sensitivity analysis reveals how robust the main findings are to the inclusion of potentially biased data.

Conclusions:

  • The developed methodology provides a robust framework for multi-arm trial meta-analysis, mitigating over-optimistic conclusions due to selection bias.
  • The approach enhances the reliability of evidence synthesis by explicitly modeling and assessing publication bias.
  • This method is crucial for accurate interpretation of treatment effectiveness, as demonstrated with antiplatelet therapy data.