The Importance of Meta-Analysis in Research: A Primer
Meta-analysis is a statistical method for combining the results of different studies on the same topic to produce a more precise estimate of an effect than any single study can provide. For researchers, students, and life-science professionals, understanding meta-analysis matters because individual studies often produce results that are not consistently reproducible, and the volume of published research has grown to the point where no single reader can synthesize it informally. This primer explains what meta-analysis is, why it holds a high position in evidence hierarchies, how to conduct one with attention to reporting standards, and where its limitations and common failure patterns lie.
At a Glance
Meta-analysis sits at the top of the evidence hierarchy because it formally, systematically, and quantitatively analyzes multiple existing research studies and synthesizes new findings based on existing data. The table below summarizes the core decisions a researcher faces when planning a meta-analysis.
| Decision Point | Standard Approach | Key Consideration |
|---|---|---|
| Research question | Define a focused question with explicit inclusion and exclusion criteria | A vague question produces an unmanageable literature search and heterogeneous study set |
| Literature search | Search multiple databases such as PubMed and NCBI Literature Resources, plus reference lists | Missing relevant studies introduces selection bias and weakens the pooled estimate |
| Effect size selection | Choose an effect size family appropriate to the outcome type | Binary, continuous, and diagnostic accuracy outcomes require different effect size metrics |
| Statistical model | Fixed effect or random effects model | Random effects models account for between-study variation and are often more appropriate when studies differ |
| Heterogeneity assessment | Quantify variation between study results | Ignoring heterogeneity can produce misleading pooled estimates |
| Bias assessment | Assess risk of bias in included studies and test for publication bias | Poor-quality primary studies cannot be corrected by meta-analysis |
| Reporting | Follow established reporting standards | Incomplete reporting of software and methods limits reproducibility |
What Meta-Analysis Is and Why It Matters
Meta-analysis is an approach to formally, systematically, and quantitatively analyze multiple existing research studies and to synthesize new research findings based upon the existing data. Until the late 1970s, meta-analyses were not regularly reported in the medical literature, but since then there has been an exponential growth of meta-analyses and they are now among the most frequently cited forms of research. A properly performed systematic review and meta-analysis is a very important tool in evidence-based medicine, and a good understanding of the steps involved is important to yield meaningful results.
The number of medical studies being published is increasing exponentially, and clinicians and researchers must routinely process large amounts of new information. The results of individual studies are often insufficient to provide confident answers because their results are not consistently reproducible. A meta-analysis is a statistical method for combining the results of different studies on the same topic, and it may resolve conflicts among studies. Meta-analysis is being used increasingly and plays an important role in medical research.
The use of meta-analysis, which is placed on top of the evidence hierarchy, in studies has been increasing exponentially. Meta-analysis has three effect size families, and using the category of effect size families helps introduce the important points in the meta-analysis process and highlights recent research trends such as network meta-analysis, meta-analytic structural equation modeling, and diagnostic test accuracy meta-analysis.
The Core Advantages of Meta-Analysis
A major advantage of a meta-analysis is that it produces a precise estimate of the effect size with considerably increased statistical power, which is important when the power of the primary study is limited because of a small sample size. A meta-analysis may yield conclusive results when individual studies are inconclusive. Furthermore, meta-analyses investigate the source of variation and different effects among subgroups. In summary, a meta-analysis is an objective, quantitative method that provides less biased estimates on a specific topic.
Meta-analysis has benefits and limitations that must be acknowledged in its application. The benefits include the ability to improve the power of small or inconclusive studies to answer questions and the ability to identify sources of diversity across various types of studies. Meta-analysis may reveal how heterogeneity among populations affects the effectiveness of medical interventions in different settings and in different patients. It can also help detect biases, such as publication bias and language bias, as well as deficiencies in the design, conduct, analysis, and interpretation of research. In this way, it can also stimulate improvements in the quality of the data needed to optimize medical care.
Meta-analysis is of fundamental importance to obtain an unbiased assessment of the available evidence. In general, the use of meta-analysis has been increasing over the last three decades, with mental health as a major research topic. It is essential to understand its methodology and interpret its results.
Meta-Analysis Versus Peer Review
Peer review is a quality control mechanism applied to individual manuscripts before publication. It assesses whether a single study is methodologically sound enough to be published. Meta-analysis operates at a different level. It evaluates a body of published and unpublished literature on a specific question and quantitatively combines the results. Peer review does not synthesize evidence across studies, and it cannot resolve conflicts between studies that reach different conclusions. Meta-analysis can resolve conflicts among studies because it formally combines their results and quantifies the overall effect.
The distinction matters for practical decisions. A clinician who reads a single peer-reviewed trial may act on a result that a subsequent meta-analysis shows to be an outlier. A researcher who relies on peer review alone may miss that the published literature on a topic is systematically biased toward positive findings. Meta-analysis provides a mechanism for detecting such biases, including publication bias, which peer review does not address.
The Systematic Review Process
Meta-analysis does not stand alone. It is the quantitative component of a systematic review, and the quality of the meta-analysis depends on the quality of the systematic review that precedes it. A step-by-step guide on conducting a quantitative systematic review using the open-source programming language R covers pre-registration, literature search and retrieval, screening, risk of bias assessment, and data extraction following the PRISMA framework. The guide also explains how to conduct both traditional and multilevel meta-analyses in R, with procedures for assessing heterogeneity, testing for publication bias, and conducting moderation analyses.
Pre-Registration and Protocol Development
Pre-registration involves publicly documenting the research question, search strategy, inclusion and exclusion criteria, and analysis plan before the review begins. This step reduces the risk that decisions made after seeing the data will bias the results. Pre-registration also distinguishes hypothesis-testing meta-analyses from exploratory ones.
Literature Search and Retrieval
The literature search should cover multiple databases. PubMed, maintained by the National Library of Medicine, and NCBI Literature Resources are standard starting points for biomedical literature. The search should also include reference lists of included studies and relevant reviews. The goal is to identify all studies that meet the inclusion criteria, beyond the ones that are easy to find.
Screening and Selection
Screening involves applying the inclusion and exclusion criteria to the titles and abstracts of identified records, then to the full texts of records that pass the first screen. Two reviewers should screen independently, and disagreements should be resolved by discussion or by a third reviewer. The screening process should be documented so that readers can see how many records were identified, how many were excluded, and why.
Risk of Bias Assessment
Risk of bias assessment evaluates the methodological quality of the included studies. Several assessment tools exist for different study designs. The Cochrane Collaboration's tool for assessing risk of bias is the best available tool for assessing randomized controlled trials. For cohort and case-control studies, the Newcastle-Ottawa Scale is recommended. The Methodological Index for Non-Randomized Studies is an excellent tool for assessing non-randomized interventional studies, and the Agency for Healthcare Research and Quality methodology checklist is applicable for cross-sectional studies. For diagnostic accuracy test studies, the Quality Assessment of Diagnostic Accuracy Studies-2 tool is recommended. The SYstematic Review Centre for Laboratory animal Experimentation risk of bias tool is available for assessing animal studies, and Assessment of Multiple Systematic Reviews is used for assessing systematic reviews themselves.
Data Extraction
Data extraction involves collecting the effect sizes, sample sizes, and study characteristics from each included study. The extraction form should be piloted on a few studies and refined before full extraction. Two reviewers should extract data independently to reduce errors.
Statistical Methods in Meta-Analysis
The statistical analysis combines the effect sizes from individual studies into a pooled estimate. The choice of statistical model and software affects the results, and these choices must be reported transparently.
Effect Size Families
Meta-analysis has three effect size families. The first family includes measures of difference between groups, such as mean differences and standardized mean differences. The second family includes measures of association, such as correlation coefficients and odds ratios. The third family includes measures of diagnostic accuracy, such as sensitivity and specificity. The choice of effect size depends on the outcome type and the research question.
Fixed Effect and Random Effects Models
A fixed effect model assumes that all studies estimate the same underlying effect and that variation between studies is due to sampling error alone. A random effects model assumes that the true effect varies between studies and that the pooled estimate represents the average of a distribution of effects. The choice between models depends on the degree of heterogeneity and the assumptions the researcher is willing to make. In practice, random effects models are often more appropriate when studies differ in populations, interventions, or settings.
Heterogeneity
Heterogeneity refers to the variation in study results beyond what would be expected by chance. Assessing heterogeneity is essential because ignoring it can produce misleading pooled estimates. Heterogeneity can be quantified using statistics that describe the proportion of total variation due to between-study variation. When heterogeneity is high, the researcher should investigate its sources through subgroup analysis or meta-regression.
Sensitivity Analysis
Sensitivity analyses test whether the results are robust to decisions made during the review. For example, a sensitivity analysis might exclude studies at high risk of bias, use a different effect size metric, or apply a different statistical model. Sensitivity analyses for missing binary outcome data and potential selection bias can be conducted with the R package metasens.
Publication Bias
Publication bias occurs when studies with statistically significant results are more likely to be published than studies with null or negative results. Meta-analysis can help detect publication bias through methods such as funnel plot asymmetry testing. However, publication bias cannot be fully corrected by statistical methods. The researcher should search for unpublished studies and report the results of publication bias tests.
Software Choices and Reporting
Meta-analysis results depend on statistical assumptions that are partly determined by the software used to conduct the analysis. Meta-epidemiological studies show that Review Manager remains the most frequently reported software in published intervention meta-analyses, but software versions, estimators, interval methods, heterogeneity specifications, and other analytical outputs are often incompletely described.
A structured comparison of selected meta-analysis software platforms used in health-related evidence synthesis includes Review Manager, Meta-DiSc, Comprehensive Meta-Analysis, R, and Stata. The comparison focuses on how these platforms implement or restrict key modeling choices relevant to intervention and effect-size meta-analysis and diagnostic test accuracy meta-analysis, including between-study variance estimation, confidence-interval construction, prediction intervals, sparse-data handling, meta-regression, and hierarchical diagnostic accuracy modeling. The platforms differ in transparency and reproducibility.
The comparison also distinguishes legacy graphical interfaces, updated web-based platforms, commercial software, and script-based environments, emphasizing how differences in flexibility, auditability, and reporting can affect interpretation. Software should be reported as part of the statistical specification of a meta-analysis, also as a computational detail. Authors, reviewers, editors, and clinical readers should recognize when software choices affect model specification, uncertainty quantification, heterogeneity assessment, sparse-data handling, diagnostic accuracy modeling, and reproducibility.
R represents a powerful and flexible tool to conduct meta-analyses. The R package meta is used to conduct standard meta-analysis. A practical tutorial describes how to perform a meta-analysis with R using a working example from the field of mental health, covering fixed effect and random effects meta-analysis, subgroup analysis, forest plots, funnel plots, and tests and adjustments for funnel plot asymmetry.
Network Meta-Analysis
There are often multiple potential interventions to treat a disease, and researchers need a method for simultaneously comparing and ranking all of these available interventions. In contrast to pairwise meta-analysis, which allows for the comparison of one intervention to another based on head-to-head data from randomized trials, network meta-analysis facilitates simultaneous comparison of the efficacy or safety of multiple interventions that may not have been directly compared in a randomized trial. Network meta-analyses help researchers study important and previously unanswerable questions, which have contributed to a rapid rise in the number of network meta-analysis publications in the biomedical literature.
When making treatment decisions, it is often necessary to consider the relative efficacy and safety of multiple potential interventions. Unlike traditional pairwise meta-analysis, which allows for a comparison between two interventions by pooling head-to-head data, network meta-analysis allows for the simultaneous comparison of more than two interventions and for comparisons to be made between interventions that have not been directly compared in a randomized controlled trial. Given these advantages, network meta-analyses are being published in the medical literature with increasing frequency.
However, the conduct and interpretation of network meta-analyses are more complex than pairwise meta-analyses. There are additional network meta-analysis model assumptions, including network connectivity, homogeneity, transitivity, and consistency, and outputs such as network plots and surface under the cumulative ranking curves. Researchers and knowledge users, including patients, clinicians, and policy makers, must consider these assumptions when conducting and evaluating a network meta-analysis. There are also multiple network meta-analysis outputs that researchers and knowledge users should understand, including network plots and mean ranks.
Applications Across Research Fields
Meta-analysis is not limited to medicine. It has been applied across the social sciences, environmental science, education, and psychology, and the methodological principles are the same.
Social Science Research
Meta-analysis in social science research follows the same principles as in medical research. A meta-analysis of leadership and workplace safety examined the associations between five leadership categories, including change-oriented, relational-oriented, task-oriented, passive, and destructive leadership, and seven workplace safety variables. Using effect sizes from 194 samples with a total of 104,364 participants, the study found that leadership behaviors are associated with workplace safety but that the leadership categories vary considerably in their relative importance. Task-oriented leadership followed by relational-oriented leadership emerged as the most important contributors to workplace safety. Change-oriented leadership, which includes transformational leadership, did not emerge as the largest contributor for any of the seven tested safety variables despite being the most frequently examined leadership model in the workplace safety literature. The effectiveness of leadership behaviors in relation to workplace safety varied by national culture power distance, industry risk, workforce age, and contextualized forms of leadership. The study also found meta-analytic evidence for publication bias and common-method variance.
Education Research
A meta-analysis in education research evaluated the impact of inquiry-based learning, problem-based learning, project-based learning, and STEM contexts on students' scientific creativity. Using the PRISMA protocol, 19 studies were analyzed. The results revealed that project-based learning and problem-based learning produced the most substantial effects, with large effect sizes, while STEM contexts and inquiry-based learning demonstrated moderate positive impacts. The limited number of studies and potential publication bias presented challenges to broader generalization.
Environmental Science
A meta-analysis of scholarly publications synthesized and updated knowledge of environmental impacts resulting from Antarctic tourism. A publication database containing 233 records focused on this topic was compiled and subjected to a general bibliometric and content analysis. An in-depth content analysis was performed on a subset of 75 records that focused on specific research on Antarctic tourism impacts. Interestingly, almost one third of the studies did not detect a direct relationship between tourism and significant negative effects on the environment. Cumulative impacts of tourism received little attention, and long-term and comprehensive monitoring programs were discussed only rarely.
Neuroimaging Research
In neuroimaging research, coordinate-based meta-analysis methods such as activation likelihood estimation combine findings from multiple functional magnetic resonance imaging studies. Activation likelihood estimation is one of the most widely employed coordinate-based meta-analysis methods and is of great importance in affective neuroscience and neuropsychology. A comprehensive review provides an introductory guide for implementing the activation likelihood estimation method in emotion research, outlining the experimental steps involved and presenting a case study about the emotion of disgust.
Reporting Standards and Quality Assessment
Several reporting standards were established for primary studies and meta-analyses. The EQUATOR Network is an international initiative that provides resources and guidelines for reporting health research, including systematic reviews and meta-analyses. Following established reporting standards is essential for transparency and reproducibility.
Critical assessment reviews have demonstrated that the current quality of reporting in some fields is low. The problematic areas include study search, study selection, risk of bias, publication bias, and additional analysis based on quality assessment. Researchers should pay particular attention to these areas when conducting and reporting meta-analyses.
The National Institute of Standards and Technology maintains the Research Data Framework, which addresses the infrastructure and practices needed to make research data findable, accessible, interoperable, and reusable. The Experimental Design Assistant from the NC3Rs is a web-based tool that helps researchers design rigorous and reproducible animal experiments, which is relevant because the quality of primary studies determines the quality of the meta-analyses that synthesize them.
Common Failure Patterns
Meta-analysis cannot improve the quality or reporting of the original studies. Other limitations come from misapplications of the method, such as when study diversity is ignored or mishandled in the analysis or when the variability of patient populations, the quality of the data, and the potential for underlying biases are not addressed.
Common failure patterns include the following:
| Failure Pattern | Description | Consequence |
|---|---|---|
| Incomplete literature search | Searching only one database or failing to search reference lists | Missing relevant studies introduces selection bias |
| Ignoring heterogeneity | Pooling studies that are too different without investigating sources of variation | Misleading pooled estimate that does not represent any real population |
| Inadequate risk of bias assessment | Failing to assess or report the methodological quality of included studies | Poor-quality primary studies produce poor-quality meta-analyses |
| Incomplete reporting of software and methods | Failing to report software versions, estimators, and heterogeneity specifications | Results cannot be reproduced or fully interpreted |
| Overlooking publication bias | Failing to test for or report publication bias | Pooled estimate is biased toward positive findings |
| Mishandling non-independent effect sizes | Treating multiple effect sizes from the same lab as independent | Standard errors are too small and confidence intervals are too narrow |
Limitations and Caveats
Meta-analysis has limitations that must be acknowledged. It cannot improve the quality or reporting of the original studies. If the primary studies are flawed, the meta-analysis will propagate those flaws. Meta-analysis also cannot address the variability of patient populations, the quality of the data, and the potential for underlying biases if these are not addressed in the primary studies.
Analyzing sources of bias and diversity is essential to performing, understanding, and using meta-analyses in medical care. Meta-analysis has promoted the sense that obtaining evidence is a global enterprise and that complete information needs to be evaluated and synthesized to obtain the most unbiased results.
The methodological quality of the included studies is a key determinant of the quality of the meta-analysis. Tools for assessing the methodological quality of randomized controlled studies are most abundant, and the choice of assessment tool should match the study design of the included studies.
Practical Steps for Conducting a Meta-Analysis
The following steps provide a practical workflow for conducting a meta-analysis. The steps follow the PRISMA framework and the guidance provided in methodological tutorials.
Step 1: Define the Research Question
Define a focused research question with explicit inclusion and exclusion criteria. The question should specify the population, intervention or exposure, comparator, outcome, and study design. A vague question produces an unmanageable literature search and a heterogeneous study set.
Step 2: Pre-Register the Protocol
Document the research question, search strategy, inclusion and exclusion criteria, and analysis plan before the review begins. Pre-registration reduces the risk of bias in decision making after the data are seen.
Step 3: Conduct the Literature Search
Search multiple databases, including PubMed and NCBI Literature Resources. Search reference lists of included studies and relevant reviews. Document the search strategy so that it can be reproduced.
Step 4: Screen and Select Studies
Apply the inclusion and exclusion criteria to titles and abstracts, then to full texts. Two reviewers should screen independently. Document the screening process with a flow diagram.
Step 5: Assess Risk of Bias
Assess the methodological quality of the included studies using a tool appropriate to the study design. Report the results of the risk of bias assessment.
Step 6: Extract Data
Extract effect sizes, sample sizes, and study characteristics from each included study. Pilot the extraction form and use two independent extractors.
Step 7: Conduct the Statistical Analysis
Choose the effect size metric and the statistical model. Assess heterogeneity. Conduct sensitivity analyses. Test for publication bias. Report the software and version used, the estimators, and the heterogeneity specifications.
Step 8: Report the Results
Follow established reporting standards. Report the search strategy, screening process, risk of bias assessment, statistical methods, and results. Provide enough detail for the analysis to be reproduced.
Records and Measurements
The following records should be maintained throughout the meta-analysis process:
| Record | Purpose | Minimum Content |
|---|---|---|
| Search log | Document the search strategy for reproducibility | Database, date, search terms, number of records retrieved |
| Screening log | Document the screening process | Number of records identified, excluded, and included at each stage |
| Risk of bias assessments | Document the methodological quality of included studies | Assessment tool, ratings for each domain, overall judgment |
| Data extraction forms | Document the data collected from each study | Effect sizes, sample sizes, study characteristics, extraction date |
| Analysis scripts | Document the statistical analysis | Software, version, code, output |
Professional Escalation Criteria
Researchers should seek additional expertise or escalate to a more experienced methodologist in the following situations:
- The research question involves multiple interventions that have not been directly compared in randomized trials, which requires network meta-analysis instead of pairwise meta-analysis.
- The included studies have non-independent effect sizes, such as multiple effect sizes from the same lab, which requires multilevel meta-analysis.
- The outcome is a diagnostic accuracy measure, which requires specialized methods and software such as Meta-DiSc.
- The heterogeneity is high and the sources of variation cannot be identified through subgroup analysis or meta-regression.
- The risk of bias in the included studies is high and cannot be addressed through sensitivity analysis.
- The researcher is uncertain about the choice of statistical model or the interpretation of the results.
Frequently Asked Questions
What is the difference between a systematic review and a meta-analysis?
A systematic review is a structured process for identifying, evaluating, and synthesizing all available evidence on a specific question. A meta-analysis is the statistical component that combines the results of the included studies into a pooled estimate. A systematic review may or may not include a meta-analysis, depending on whether the studies are sufficiently similar to combine.
How is meta-analysis different from peer review?
Peer review is a quality control mechanism applied to individual manuscripts before publication. It assesses whether a single study is methodologically sound. Meta-analysis evaluates a body of literature on a specific question and quantitatively combines the results. Peer review does not synthesize evidence across studies and cannot resolve conflicts between studies that reach different conclusions.
What is publication bias and how does it affect meta-analysis?
Publication bias occurs when studies with statistically significant results are more likely to be published than studies with null or negative results. This means the published literature may not represent all the evidence that exists. Meta-analysis can help detect publication bias through methods such as funnel plot asymmetry testing, but it cannot fully correct for the bias.
What is heterogeneity in meta-analysis?
Heterogeneity refers to the variation in study results beyond what would be expected by chance. It can arise from differences in populations, interventions, comparators, outcomes, or study designs. Assessing heterogeneity is essential because ignoring it can produce misleading pooled estimates. When heterogeneity is high, the researcher should investigate its sources through subgroup analysis or meta-regression.
What is the difference between fixed effect and random effects models?
A fixed effect model assumes that all studies estimate the same underlying effect and that variation between studies is due to sampling error alone. A random effects model assumes that the true effect varies between studies and that the pooled estimate represents the average of a distribution of effects. Random effects models are often more appropriate when studies differ in populations, interventions, or settings.
What is network meta-analysis?
Network meta-analysis is an extension of pairwise meta-analysis that allows for the simultaneous comparison of more than two interventions. It can make comparisons between interventions that have not been directly compared in a randomized controlled trial. Network meta-analysis requires additional assumptions, including network connectivity, homogeneity, transitivity, and consistency.
What software can I use to conduct a meta-analysis?
Common software platforms include Review Manager, Meta-DiSc, Comprehensive Meta-Analysis, R, and Stata. R is a freely available statistical software environment that is powerful and flexible. The R package meta is used to conduct standard meta-analysis, and the R package metasens is used for sensitivity analyses. The choice of software affects the statistical assumptions and should be reported as part of the statistical specification.
How do I know if a meta-analysis is trustworthy?
Assess whether the authors followed established reporting standards, conducted a comprehensive literature search, assessed the risk of bias of the included studies, investigated heterogeneity, tested for publication bias, and reported the software and statistical methods in enough detail to be reproduced. The EQUATOR Network provides resources and guidelines for reporting health research, including systematic reviews and meta-analyses.
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References and Further Reading
- Research Data Framework. National Institute of Standards and Technology.
- EQUATOR Network. EQUATOR Network.
- Experimental Design Assistant. NC3Rs.
- NCBI Literature Resources. National Center for Biotechnology Information.
- PubMed. National Library of Medicine.
- An overview of meta-analysis for clinicians.. The Korean journal of internal medicine, 2018.
- How to perform a meta-analysis with R: a practical tutorial.. Evidence-based mental health, 2019.
- Network Meta-Analysis.. Methods in molecular biology (Clifton, N.J.), 2022.
- Research Techniques Made Simple: Network Meta-Analysis.. The Journal of investigative dermatology, 2019.
- Basics of meta-analysis.. Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology, 2020.
- Recent Research Trends in Meta-analysis.. Asian nursing research, 2017.
- The methodological quality assessment tools for preclinical and clinical studies, systematic review and meta-analysis, and clinical practice guideline: a systematic review.. Journal of evidence-based medicine, 2015.
- Pooling research results: benefits and limitations of meta-analysis.. The Joint Commission journal on quality improvement, 1999.
- Reporting and interpreting meta-analysis software choices: practical guidance for researchers, reviewers, and editors.. 2026.
- Reporting and Interpreting Meta-Analysis Software Choices: Practical Guidance for Researchers, Reviewers, and Editors. 2026.
- From Review to Synthesis: A Step-by-Step Methodological Guide to Systematic Reviews and Multilevel Meta- Analyses. 2026.
- From Review to Synthesis: A Step-by-Step Methodological Guide to Systematic Reviews and Multilevel Meta- Analyses. 2026.
- Activation Likelihood Estimation Neuroimaging Meta-Analysis: a Powerful Tool for Emotion Research. Psychology Research and Behavior Management, 2024.
- Fostering scientific creativity in science education through scientific problem-solving approaches and STEM contexts: a meta-analysis. Disciplinary and Interdisciplinary Science Education Research, 2025.
- A meta-analysis of leadership and workplace safety: Examining relative importance, contextual contingencies, and methodological moderators.. Journal of Applied Psychology, 2022.
- What are the real environmental impacts of Antarctic tourism? Unveiling their importance through a comprehensive meta-analysis.. Journal of Environmental Management, 2022.
- Network meta-analysis in psychiatric research: Opportunities and caveats. Psychiatria Danubina, 2018.
- No study left behind: A network meta-analysis in non-small-cell lung cancer demonstrating the importance of considering all relevant data. Value in Health, 2009.
- Meta-analysis: An approach to synthesizing and evaluating research on knowledge and information science. Iranian Journal of Information Processing Management, 2014.
- The role of method in treatment effectiveness research: Evidence from meta-analysis. Psychological Methods, 2001.
- The use of meta-analysis or research synthesis to combine driving simulation or naturalistic study results on driver distraction. Journal of Safety Research, 2014.
This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.