How to Conduct a Meta-Analysis in 2026: A PRISMA 2020 Protocol

High-Impact Journals

Interesting topics

How to Conduct a Meta-Analysis in 2026: A PRISMA 2020 Protocol

Today, meta-analysis is regarded as one of the most reliable ways of synthesising evidence obtained through multiple independent studies and deriving reliable results. With the development of artificial intelligence (AI) and software for systematic reviews and statistical analysis, in 2026, meta-analysis is becoming more efficient and more open. People working in different fields, including health care, social sciences, engineering, business, and environmental sciences, apply meta-analysis to make their decisions evidence-based.

The process of conducting a meta-analysis is not limited to the synthesis of results obtained in previous research only. It requires many other important steps, including planning, searching the literature, data extraction, statistical analysis, and reporting. Adhering to international reporting guidelines, such as PRISMA 2020, helps to ensure the reliability of results. Understanding how to conduct a meta-analysis requires following a structured process that includes research planning, literature searching, data extraction, statistical analysis, and transparent reporting using guidelines such as the PRISMA 2020 protocol and a PRISMA flow diagram.[1]

1. What Is a Meta-Analysis and Why Does It Matter in 2026?

Meta-analysis is a research approach that involves combining statistical findings from different studies focused on the same issue. In contrast to a traditional literature review, a meta-analysis provides the researcher with a pooled effect size, thus helping him or her draw more accurate conclusions. The difference between systematic review vs meta-analysis is important because a systematic review identifies and evaluates available evidence, whereas meta-analysis statistically combines numerical results from selected studies. [2]

With the help of modern digital databases for conducting research, as well as advanced statistical software, meta-analysis has become easier to conduct nowadays. Many healthcare agencies, policymakers, and pharmaceutical companies use meta-analysis to formulate evidence-based guidelines and determine what needs to be researched in the future.

2. Planning a High-Quality Meta-Analysis

The research protocol will include the aims and objectives of the study, criteria for inclusion/exclusion, search techniques, outcomes, and statistical methods before data collection. Registration of the research protocol on websites such as PROSPERO promotes transparency of research and prevents reporting biases. A well-developed PRISMA 2020 protocol supports researchers in maintaining methodological transparency throughout the review process.

3. Finding and Selecting Relevant Studies

Literature search is the first step for conducting meta-analysis. Researchers need to search more than one electronic database based on the research discipline, such as PubMed, Scopus, Web of Science, Embase, IEEE Xplore, and Google Scholar.

Good search strategy consists of keywords and controlled vocabulary, Boolean operators, and inclusion/exclusion criteria. All retrieved studies are screened through titles and abstracts and full papers to assess their eligibility.

It is important to conduct a study based on the PRISMA flow diagram 2020 statement to report how articles are selected.

4. Extracting and Managing Research Data

After selecting the eligible studies, researchers then collect standardised data from each individual publication. Common variables include design, number of participants, participant description, intervention, outcomes, follow-up time, and statistics reported.

The accurate extraction of data helps to prevent errors in the statistical analysis process. In recent years, many researchers have been using software specifically designed for data management, including Covidence, Rayyan, Rev Man, and Distiller SR.

Documentation of the process is very important for reproducibility. During this stage of how to conduct a meta-analysis, researchers must ensure that extracted information is accurate, consistent, and suitable for further statistical evaluation.[3]

5. Statistical Analysis and Data Synthesis

The statistical synthesis process involves summarising the results obtained by individual studies in a single pooled result. The researchers choose the correct measures for effect sizes according to the nature of the outcomes in terms of odds ratio, risk ratio, mean difference, standardised mean difference, and hazard ratio.

t is very important to analyse the heterogeneity in meta-analysis. This is because heterogeneity indicates the variability that exists among the studies selected for analysis. There are several statistical measures that help in deciding which model is suitable: fixed or random. A forest plot represents all the information concerning pooled effect sizes, confidence interval, and the contribution of each study towards the results.[4]

Statistical Measure	Primary Purpose
Odds Ratio (OR)	Binary outcomes
Risk Ratio (RR)	Treatment comparison
Mean Difference (MD)	Continuous variables
Standardised Mean Difference (SMD)	Different measurement scales
I² Statistic	Measures heterogeneity
Forest Plot	Visualises pooled results

6. Interpreting Results and Assessing Study Quality

Correct interpretation is more than just statistical significance. The quality and bias risk of the studies included should be assessed using well-known tools such as the Cochrane Risk of Bias Tool, ROBINS-I, or the Newcastle-Ottawa scale.

Publication bias is also to be considered by means of the funnel plot or statistical tests like Egger’s regression. Proper interpretation involves considering the level of evidence, clinical/practical applicability, limitations of the study, and the perspective for future research instead of just figures.[5]

7. Common Challenges and Best Practices

Although meta-analysis provides high-quality evidence, researchers often encounter methodological challenges that can influence study validity.

Challenge	Best Practice
Publication bias	Perform funnel plot analysis
High heterogeneity	Conduct subgroup and sensitivity analyses
Incomplete reporting	Contact original study authors
Poor-quality studies	Apply standardised quality assessment tools
Data extraction errors	Use duplicate independent reviewers
Selective reporting	Follow PRISMA 2020 reporting guidelines

To improve research quality, researchers should maintain predefined protocols, use multiple reviewers during study selection and data extraction, validate statistical analyses, and report methods transparently. Combining methodological rigour with appropriate statistical techniques enhances the reliability and reproducibility of meta-analysis findings.

8. Future Trends in Meta-Analysis Research

Meta-analysis is evolving through advances in artificial intelligence and automation. AI streamlines literature screening, duplicate detection, citation management, and data extraction, making systematic reviews faster and more efficient. Emerging approaches such as network meta-analysis, individual participant data meta-analysis, and living systematic reviews enable continuous updates with new evidence. By combining AI with rigorous methodological standards, future meta-analyses will provide faster, broader, and more reliable evidence for research, policy, and practice.

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

A meta-analysis is still among the best approaches for evidence synthesis in 2026. For an effective meta-analysis, it is necessary to plan, search, extract data, analyse, assess the quality of the studies, and report results systematically. Despite existing problems like publication bias and heterogeneity of the studies, nowadays it is possible to use modern analytical methods and global guidelines for obtaining reproducible results. The combination of good methodology and advanced AI technologies will allow meta-analysis to continue its role in science.

How to Conduct a Meta-Analysis in 2026: An 8-Step PRISMA 2020 Protocol. Our Pubrica consultants are here to guide you. [Get Expert Publishing Support] or [Schedule a Free Consultation]

Frequently Asked Questions (FAQs)

1. What is a meta-analysis and why is it important in 2026?

A meta-analysis is a statistical method that combines results from multiple independent studies addressing the same research question. It provides a pooled effect size, improves the accuracy of conclusions, and helps researchers, healthcare professionals, policymakers, and organisations make evidence-based decisions. In 2026, advances in artificial intelligence (AI), automation, and statistical software have made meta-analysis more efficient, transparent, and accessible.

2. What is the difference between a systematic review and a meta-analysis?

A systematic review is a structured process of identifying, evaluating, and summarising all available research evidence related to a specific question. A meta-analysis is a statistical technique that may be included within a systematic review to combine numerical results from eligible studies. While all meta-analyses require a systematic approach, not all systematic reviews include a meta-analysis.

3. What are the main steps involved in conducting a meta-analysis?

The main steps in conducting a meta-analysis include developing a research question and protocol, searching relevant literature, selecting eligible studies, extracting data, calculating effect sizes, performing statistical analysis, assessing study quality and bias, interpreting results, and reporting findings according to guidelines such as the PRISMA 2020 protocol.

4. What is the role of PRISMA 2020 in meta-analysis?

PRISMA 2020 provides internationally recognised reporting guidelines for systematic reviews and meta-analyses. It helps researchers report their methods and findings transparently, including details about study identification, screening, eligibility assessment, and inclusion. A PRISMA flow diagram is commonly used to show the study selection process.

5. What is effect size calculation in meta-analysis?

Effect size calculation is the process of measuring the strength and direction of the relationship or difference between groups across different studies. Common effect size measures include odds ratio (OR), risk ratio (RR), mean difference (MD), standardised mean difference (SMD), and hazard ratio (HR). Effect size calculation allows researchers to combine findings from studies using different sample sizes and measurement methods.

6. What is a forest plot in meta-analysis?

A forest plot is a graphical presentation of meta-analysis results. It displays the effect size and confidence interval of each individual study, along with the overall pooled effect estimate. Forest plots help researchers understand the consistency of study findings, compare individual results, and evaluate the overall evidence from the meta-analysis.

References

Calderon Martinez, E., Ghattas Hasbun, P. E., Salolin Vargas, V. P., García-González, O. Y., Fermin Madera, M. D., Rueda Capistrán, D. E., Campos Carmona, T., Sanchez Cruz, C., & Teran Hooper, C. (2025). A comprehensive guide to conduct a systematic review and meta-analysis in medical research. Medicine, 104(33), e41868. https://doi.org/10.1097/MD.0000000000041868
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
Surkis, A., & Read, K. (2015). Research data management. Journal of the Medical Library Association : JMLA, 103(3), 154–156. https://doi.org/10.3163/1536-5050.103.3.011
Schick-Makaroff, K., MacDonald, M., Plummer, M., Burgess, J., & Neander, W. (2016). What Synthesis Methodology Should I Use? A Review and Analysis of Approaches to Research Synthesis. AIMS public health, 3(1), 172–215. https://doi.org/10.3934/publichealth.2016.1.172
Delavari, S., Pourahmadi, M., & Barzkar, F. (2023). What Quality Assessment Tool Should I Use? A Practical Guide for Systematic Reviews Authors. Iranian journal of medical sciences, 48(3), 229–231. https://doi.org/10.30476/IJMS.2023.98401.3038