Targeted literature searches are a fundamental part of writing clinical manuscripts that will meet the standards of high-quality journals and contribute meaningfully to evidence-based practice. When physicians write clinical manuscripts, utilizing a targeted literature search can identify high-quality, relevant, and current evidence. While a general literature review is useful, a targeted literature search is specific to the clinical question and should be completed through frameworks established, such as PICO (Population, Intervention, Comparator, Outcome) and PRISMA [1].
Meta-analysis made easy: 6 Essential statistical considerations
- Home
- Academy
- Research Impact
- Meta-analysis made easy: 6 Essential statistical considerations
High-Impact Journals
Interesting topics
Meta-analysis made easy: 6 Essential statistical considerations
1. What is meta-analysis?
Meta-analysis is a statistical method that allows for the integration of evidence provided by various studies, all for the purpose of enhancing the accuracy of current information, answering questions that individual studies cannot, and resolving semantically conflicting science or evidence. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) is an excellent guide if you wish to report a meta-analysis in your research manuscript.
Meta-analyses are usually displayed using a forest plot, which is a visual way of displaying effect estimates and confidence intervals for individual studies, and the overall meta-analysis. [1],[2]
2. Statistical Considerations in Medical Meta-Analysis
Meta-analyses can influence the decisions made by clinicians, researchers, policymakers, etc., so it is important that meta-analyses are performed in a correct and rigorous manner. Below, we have outlined some of the major steps you need to take when conducting a meta-analysis. [3]
3. Research Question
Construct a strong and precise research question objective. This enables you to understand what data you will need to extract and what variables you will be looking at. For example, interventions for diabetes management are a very broad topic. it is preferable to narrow it down to exercise interventions to improve glycaemic control or vitamin D supplementation to improve glucose metabolism.
4. Defining Outcomes
Identify and define all outcomes for which you will collect data. Two studies on the same intervention may collect data on different outcomes e.g., one study on a weight-loss intervention might look at percentage of weight changed while another study on the same weight loss intervention might look at change in body mass index.
5. Handling Missing Data
Follow a specific algorithm for handling missing summary statistics, missing variable conversions, etc., as part of preserving data integrity. Using multiple imputation, inverse-probability macro-adjusting, or possibly using complete-case analysis are all potential methodologies. You will ultimately, in your research manuscript, also be responsible for reporting the programmatic methods you employed in the methodology section.
6. Heterogeneity
When conducting a meta-analysis, statistical heterogeneity should always be calculated and reported. Heterogeneity (often calculated using Cochran’s Q and I2) provides your readers with a sense of the degree of observed variation in outcomes or intervention effects between studies. Some heterogeneity is to be expected between studies included in a meta-analysis, but it needs to be measured and reported. [4]
7. Risk of Bias
Always assess and report risk of bias in each included study. Because the quality of the studies being included in a meta-analysis may vary, it is important to assess the design, execution, and report of all the studies to determine if there are elements that could cause biases in the studies. Assessing risk of bias gives you an understanding of the quality of the evidence provided by the different studies and allows for an assessment for certainty of the overall effect size estimate. [5]
8. Sensitivity
Perform and present a sensitivity analysis sensitivity testing. Sensitivity analysis is valuable to the reader to aid in their understanding of the degree of robustness of the overall findings of your meta-analysis, and the degree to which individual studies influenced the overall findings. Sensitivity analysis may also help you identify outlier studies (i.e., studies that indicated unusually large or small effects relative to similar studies). [6]
Connect with us to explore how we can support you in maintaining academic integrity and enhancing the visibility of your research across the world!
Conclusion
Meta-analysis is a rigorous statistical method that combines data from several studies to yield a more precise estimator of intervention effects or associations. A robust meta-analysis is dependent on the careful development of a clear research question, identified and defined outcomes, appropriate methods of addressing missing data, assessing heterogeneity, evaluating risk of bias, and sensitivity analyses.
Meta-analysis made easy: 6 Essential statistical considerations? Pubrica offers end-to-end research design, analysis, and reporting support
References
- Berman, N. G., & Parker, R. A. (2002). Meta-analysis: neither quick nor easy. BMC medical research methodology, 2, 10. https://doi.org/10.1186/1471-2288-2-10
- What is Meta-Analysis? Definition, Research, Examples. (2024, February 1). Retrieved August 29, 2025, from Appinio.com website: https://www.appinio.com/en/blog/market-research/meta-analysis
- Barza, M., Trikalinos, T. A., & Lau, J. (2009). Statistical considerations in meta-analysis. Infectious disease clinics of North America, 23(2), . https://doi.org/10.1016/j.idc.2009.01.003
- Testing publication bias and heterogeneity in meta-analysis when using multiple studies. (n.d.). Retrieved August 29, 2025, from Cross Validated website: https://stats.stackexchange.com/questions/636822/testing-publication-bias-and-heterogeneity-in-meta-analysis-when-using-multiple
- Hempel, S., Miles, J. N. V., Booth, M. J., Wang, Z., Morton, S. C., and Shekelle, P. G. (2013). Risk of bias: a simulation study of power to detect study-level moderator effects in meta-analysis. Systematic Reviews, 2(1), 107. https://doi.org/10.1186/2046-4053-2-107
- Deeks, J. J., & Altman, D. G. (1999). Sensitivity and specificity and their confidence intervals cannot exceed 100%. BMJ (Clinical research ed.), 318(7177), 193–194. https://doi.org/10.1136/bmj.318.7177.193b