Mental Health Awareness Offer

Discover your mystery discount!

Meta-analysis

1. Meta-analysis Basics: – Coined in 1976 by statistician Gene Glass. – Aimed to describe aggregated measures of relationships and effects. – Growth in meta-analysis: […]

« Back to Glossary Index

1. Meta-analysis Basics:

– Coined in 1976 by statistician Gene Glass.
– Aimed to describe aggregated measures of relationships and effects.
– Growth in meta-analysis: 334 published by 1991, 9,135 by 2014.
– Steps include systematic review, research question formulation, literature search, study selection, and quality criteria consideration.
– Model selection, study heterogeneity examination, and inclusion of unpublished studies to avoid bias.

2. Data Collection and Analysis:

– Use of appropriate keywords, search limits, and Boolean operators in literature search.
– Standardized data collection form for eligible studies.
– Collection of effect size information using Pearson’s statistic.
– Inclusion of study characteristics that may moderate effects.
– Assessment of study quality and risk of bias using available tools.

3. Gray Literature and Statistical Models:

– Inclusion of gray literature reduces publication bias risk.
– Two types of evidence in meta-analysis: individual participant data (IPD) and aggregate data (AD).
– Statistical models for aggregate data: fixed effect, random effects, and IVhet models.
– Different meta-analytic methods for IPD evidence synthesis.
– Advanced techniques like restricted maximum likelihood estimator and software platforms for meta-analysis.

4. Advanced Meta-analysis Models:

– Quality Effects Model introduced by Doi and Thalib.
– Network Meta-Analysis Methods including the Bucher method and Bayesian framework.
– Validation of meta-analysis results and challenges in meta-analysis.
– Publication bias and problems related to non-statistically significant effects.
– Statistical approaches and agenda-driven bias in meta-analysis.

5. Statistical Frameworks and Challenges:

– Bayesian Framework involving WinBUGS and R software.
– Frequentist Multivariate Framework and generalized pairwise modeling.
– Challenges in distinguishing between analysis model and data-generation mechanism.
– Problems related to agenda-driven bias.
– Problems arising from studies not reporting non-statistically significant effects.

Meta-analysis (Wikipedia)

Meta-analysis is the statistical combination of the results of multiple studies addressing a similar research question. An important part of this method involves computing an effect size across all of the studies; this involves extracting effect sizes and variance measures from various studies. Meta-analyses are integral in supporting research grant proposals, shaping treatment guidelines, and influencing health policies. They are also pivotal in summarizing existing research to guide future studies, thereby cementing their role as a fundamental methodology in metascience. Meta-analyses are often, but not always, important components of a systematic review procedure. For instance, a meta-analysis may be conducted on several clinical trials of a medical treatment, in an effort to obtain a better understanding of how well the treatment works.

A meta-analysis is the highest form of knowledge in science
Graphical summary of a meta-analysis of over 1,000 cases of diffuse intrinsic pontine glioma and other pediatric gliomas, in which information about the mutations involved as well as generic outcomes were distilled from the underlying primary literature.
« Back to Glossary Index
This site uses cookies to offer you a better browsing experience. By browsing this website, you agree to our use of cookies.