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Meta-Analysis
A meta-analysis is a specific type of systematic review that includes a quantitative pooling of data. Although the term dates back to 1976, use of these techniques greatly expanded with the foundation of the Cochrane Collaboration in 1993. Meta-analyses rely on secondary or published data, thereby removing the need for further approval from an ethics committee. Meta-analyses and systematic reviews of randomized controlled trials (RCTs) are the most robust form of research design in the hierarchy of research evidence, with Cochrane systematic reviews representing the gold standard for these methodologies. However, these techniques can also be applied to observational studies.
This introduction to meta-analyses describes the process of undertaking a review of either RCTs or observational studies. Tasks outlined include registering the title and protocol with an appropriate registry such as the Cochrane Collaboration or PROSPERO and then undertaking the review based on that protocol. The use of appropriate meta-analytical techniques are described including appropriate tests to use for continuous and dichotomous variables (e.g., mean difference [MD], relative risks, or odds ratios [ORs]), and the use of fixed or random effects. Examples of bibliographic software and freeware for meta-analyses such as Review Manager (RevMan) or WINPEPI are also highlighted.
Why Do a Meta-Analysis?
By pooling the results from multiple studies, a meta-analysis increases statistical power and hence the chance of detecting a statistically significant effect, if it exists. In particular, including patients from a number of trials increases the chances of detecting an effect that may have not have otherwise been detected in a small individual trial.
Good Practice in Reporting a Meta-Analysis
There are several important issues to consider before any data analysis is performed. Crucially, these will enhance the quality of subsequent findings.
Use of the Appropriate Guidelines for Reporting a Systematic Review
Guidelines for meta-analyses have been established and accepted, and following such accepted guidelines is a requirement for publication in many journals. For meta-analyses of RCTs, these guidelines are compiled in a statement called the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA; see Table 1). Although this statement focuses on randomized trials, it can also be used for other designs, such as service evaluation, and consists of a checklist and flow diagram. For observational studies, the appropriate recommendations are compiled in the Meta-analyses of Observational Studies in Epidemiology (MOOSE) statement. A disadvantage of these tools is that they only apply to specific aspects and types of reviews, as opposed to being global guidelines (see Table 1). As a result, a Measurement Tool to Assess Systematic Reviews has been developed to evaluate systematic reviews that include both randomized and/or nonrandomized studies (see Table 1). Finally, the National Institute of Health has produced a tool that can be applied to a broad range of systematic reviews and meta-analyses whether these are of observational or experimental studies (see Table 1). Given the range of alternatives, it is advisable to pick the option that best suits a particular meta-analysis.
Prospective Registration of a Protocol
Authors of meta-analyses typically register the review’s protocol with an appropriate repository or registry. In the case of Cochrane reviews, this is a mandatory step prior to completion of the meta-analysis. For other meta-analyses, an alternative is PROSPERO (see Table 1). PROSPERO is an online repository of protocols similar to clinical trial registries or protocols in the Cochrane Library, hosted by the Centre for Reviews and Dissemination at the University of York. These are shorter than Cochrane protocols and cover the study design of interest, the condition under review, the target population, any comparison group, relevant outcomes, and any proposed intervention. The proposed search terms, methods of data extraction, and methods of assessing study quality are also required. All accepted meta-analyses are given a registration number, which is then cited in the published meta-analysis. Open science framework and some clinical trial registries also accept protocols for meta-analysis (see Table 1). Another option is researchregistry.com, which unlike the previously mentioned repositories, charges a fee for each registration.
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- Bayesian Statistics
- Descriptive Statistics
- Central Tendency, Measures of
- Cohen’s d Statistic
- Cohen’s f Statistic
- Correspondence Analysis
- Descriptive Statistics
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- Important Publications
- “Coefficient Alpha and the Internal Structure of Tests”
- “Convergent and Discriminant Validation by the Multitrait–Multimethod Matrix”
- “Meta-Analysis of Psychotherapy Outcome Studies”
- “On the Theory of Scales of Measurement”
- “Probable Error of a Mean, The”
- “Psychometric Experiments”
- “Sequential Tests of Statistical Hypotheses”
- “Structural Holes: The Social Structure of Competition”
- “Technique for the Measurement of Attitudes, A”
- “Validity”
- Aptitudes and Instructional Methods
- Doctrine of Chances, The
- Logic of Scientific Discovery, The
- Nonparametric Statistics for the Behavioral Sciences
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- Social Network Analysis Methodsand Applications
- Statistical Power Analysis for the Behavioral Sciences
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- Structural Equivalence of Individuals in Social Networks
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- Underrepresented Groups
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- Chi-Square Test
- Cluster Analysis
- Confirmatory Factor Analysis
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- Descriptive Discriminant Analysis
- Diagnostic Classification Modeling
- Dummy Coding
- Duncan’s Multiple Range Test
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- Effect Coding
- Estimation
- Exploratory Factor Analysis
- Fisher’s Least Significant Difference Test
- Friedman Test
- Greenhouse–Geisser Correction
- Hidden Markov Model
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- Holm’s Sequential Bonferroni Procedure
- Jackknife
- Kolmogorov–Smirnov Test
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- Least Squares, Methods of
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- Mann–Whitney U Test
- Mauchly Test
- Maximum Likelihood Estimation
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- Mean Comparisons
- Missing Data, Imputation of
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- Multiple Comparison Tests
- Multiple Comparisons With Modeling Techniques
- Multiple Regression
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- Newman–Keuls Test
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- Pairwise Comparisons
- Path Analysis
- Post Hoc Analysis
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- Predictive Discriminant Analysis
- Principal Components Analysis
- Propensity Score Analysis
- Scheffé Test
- Sequential Analysis
- Sign Test
- Stepwise Model Selection
- Stepwise Regression
- Structural Equation Modeling
- Survival Analysis
- Trend Analysis
- Tukey’s Honestly Significant Difference
- Welch’s t Test
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- Yates’s Correction
- Statistical Tests
- F Test
- t Test, Independent Samples
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- z Test
- Bartlett’s Test
- Behrens–Fisher t’ Statistic
- Chi-Square Test
- Cochran–Armitage Test for Trend
- Duncan’s Multiple Range Test
- Dunnett’s Test
- Fisher’s Least Significant Difference Test
- Friedman Test
- Hosmer-Lemeshow Test
- Kolmogorov–Smirnov Test
- Kruskal–Wallis Test
- Mann–Whitney U Test
- Mauchly Test
- McNemar’s Test
- Multiple Comparison Tests
- Newman–Keuls Test
- Omnibus Tests
- Scheffé Test
- Sign Test
- Tukey’s Honestly Significant Difference
- Welch’s t Test
- Wilcoxon Rank Sum Test
- Structural Equation Modeling
- Theories, Laws, and Principles
- Central Limit Theorem
- Classical Test Theory
- Correspondence Principle
- Critical Theory
- Diffusion of Innovation Theory
- Falsifiability
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- Gauss–Markov Theorem
- Generalizability Theory
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- Machine Learning
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- Occam’s Razor
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- Toulmin Method
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- Types of Variables
- Validity of Scores
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