A meta-analysis uses statistical methods to summarize the results of a systematic review.
- Meta-analysis = study of studies
- Meta-analyses use statistical methods to analyze the findings in multiple studies
- Meta-analyses can include a pooled estimate and confidence interval for the treatment effect after combining all the studies; test for whether the treatment or risk factor effect is statistically significant and test for heterogeneity between the studies
Determine eligibility for Meta-Analysis #
Study Type and Size
- Consistency of Study Type: Ensure that the majority of studies share a similar design (e.g., randomized controlled trials, observational studies). Inconsistent study types can introduce heterogeneity and limit the ability to draw robust conclusions.
- Sample Size: While a minimum sample size is not universally defined, studies with extremely small sample sizes (e.g., fewer than 5 participants) may lack sufficient statistical power to detect meaningful differences. However, the appropriate sample size can vary depending on the specific research question, the expected effect size, and the variability of the outcome measure.
Clinical Heterogeneity
- Patient Characteristics: Consider whether the patient populations across studies are comparable. Differences in age, gender, ethnicity, disease severity, or comorbidities can influence treatment outcomes and introduce heterogeneity.
- Study Interventions: Assess the consistency of the interventions being compared. Variations in dosage, duration, or administration methods can affect treatment effects.
- Outcome Measures: Ensure that the studies measure the same or similar outcomes using comparable methods. Inconsistent outcome definitions or measurement tools can hinder the ability to combine results.
Overall Expected Study Size
- Statistical Power: A meta-analysis with too few studies may lack sufficient statistical power to detect meaningful differences. Conversely, an excessively large meta-analysis can be cumbersome and may not provide additional insights.
- Practical Considerations: Consider the time and resources required to conduct a meta-analysis. A large number of studies may increase the workload and complexity of the analysis.
- Client Requirements: The desired level of precision and statistical power should be considered when determining the appropriate number of studies.
Additional Considerations
- Publication Bias: Be aware of publication bias, which can occur when studies with positive or statistically significant results are more likely to be published than those with negative or non-significant results. This can distort the overall evidence base.
- Quality Assessment: Consider conducting a quality assessment of the included studies to identify potential methodological flaws that may affect the validity of the results.
By carefully evaluating these factors, you can determine whether a meta-analysis is feasible and appropriate for your research question.
Meta-Analysis Software #
Different software applications are able to perform a meta-analysis (Muka et al., 2020).
STATA #
The “metan” command in STATA is a simple way to perform a meta-analysis: Meta-analysis in Stata
R #
The meta-analysis package “meta” in R is a free way to perform meta-analyses. Meta package
RevMan #
Review Manager (RevMan) was developed by the Cochrane Collaboration. RevMan
Excel #
MetaEasy is an add-in for Microsoft Excel that automates many meta-analysis processes and provides support for the task. MetaEasy download for Excel
Reference: Kontopantelis E and Reeves D. MetaEasy: A Meta-Analysis Add-In for Microsoft Excel. Journal of Statistical Software 2009;30:1 – 25. https://www.jstatsoft.org/article/view/v030i07
