The Purpose of Meta-Analysis: Why Synthesize Studies Quantitatively?

Meta-analysis is a formal, systematic, and quantitative approach to combining results from multiple existing research studies to synthesize new findings based on the existing data. Until the late 1970s, meta-analyses were not regularly reported in the medical literature, but since then there has been exponential growth, 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 understanding the steps involved is important to yield meaningful results. This article explains the core purposes of meta-analysis, such as increasing statistical power, resolving conflicting findings, and identifying moderators, with examples from the life sciences. It provides a conceptual framework to help researchers articulate the rationale for conducting a meta-analysis in their own work.

At a Glance: Core Purposes of Meta-Analysis

What Meta-Analysis Is and What It Is Not

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. It is a statistical technique that combines the results of separate studies into a single pooled estimate. The key word is quantitative. A meta-analysis does not simply narrate what studies found. It applies statistical methods to weight each study by its precision and sample size, then calculates an overall effect size with a confidence interval.

A systematic review is a broader process. Systematic reviews are a type of evidence synthesis in which authors develop explicit eligibility criteria, collect all the available studies that meet these criteria, and summarize results using reproducible methods that minimize biases and errors. Systematic reviews can address questions regarding effects of interventions or exposures, diagnostic properties of tests, and prevalence or prognosis of diseases. All rigorous systematic reviews have common processes that include determining the question and eligibility criteria, including a priori specification of subgroup hypotheses, searching for evidence and selecting studies, abstracting data and assessing risk of bias of the included studies, summarizing the data for each outcome of interest, whenever possible using meta-analyses, and assessing the certainty of the evidence and drawing conclusions.

Meta-analysis is one component of a systematic review, but not every systematic review includes a meta-analysis. When studies are too heterogeneous in their designs, populations, or outcome measures, a narrative synthesis may be more appropriate. The distinction matters because readers and reviewers must recognize that the quality and strength of recommendations in a review are only as strong as the quality of studies that it analyzes. Great care must be used in the interpretation of bias and extrapolation of the review's findings to translation to clinical practice.

Increasing Statistical Power Through Pooling

The most fundamental purpose of meta-analysis is to increase statistical power. Individual studies, particularly in fields like psychiatry, pediatrics, and ecology, are often underpowered to detect small but clinically meaningful effects. A study with 50 participants per group may not have enough statistical power to detect a modest difference between treatment and control. When 10 such studies are combined, the pooled sample size may be sufficient to detect that same effect with confidence.

The umbrella review of attention deficit hyperactivity disorder (ADHD) prevalence illustrates this principle. Emerging epidemiological data suggest that hundreds of primary studies have examined the prevalence of ADHD in children and adolescents, and dozens of systematic reviews and meta-analyses have been conducted on the subject. The umbrella review included 13 meta-analytic systematic reviews with 588 primary studies and 3,277,590 participants. A random effect meta-analysis of these studies showed that the global prevalence of ADHD in children and adolescents was 8.0 percent with a 95 percent confidence interval of 6.0 to 10 percent. No single primary study could provide an estimate with this level of precision. The pooled analysis also revealed that the prevalence estimate was twice higher in boys at 10 percent compared to girls at 5 percent, and that the inattentive type of ADHD was the most common subtype.

The same logic applies across the life sciences. A meta-analysis of randomized clinical trials reported a small but significant reduction in nausea and vomiting from various causes when comparing prescribed cannabinoids with placebo or active comparators, with a standardized mean difference of negative 0.29 and a 95 percent confidence interval of negative 0.39 to negative 0.18. Another meta-analysis of randomized clinical trials among patients with HIV/AIDS reported that cannabinoids had a moderate effect on increasing body weight compared with placebo, with a standardized mean difference of 0.57 and a 95 percent confidence interval of 0.22 to 0.92. These effect sizes are modest, and many individual trials would not have had sufficient power to detect them reliably.

Resolving Conflicting Findings Across Studies

A second core purpose of meta-analysis is to resolve conflicting findings. Researchers in any field can recall examples where one study found a significant effect and another found none. These apparent contradictions can arise from sampling error, differences in study design, differences in populations, or differences in outcome measurement. A meta-analysis provides a formal framework for determining whether the conflicting results are compatible with a single underlying effect or whether they reflect genuine differences between studies.

The systematic review and meta-analysis of co-infections in people with COVID-19 demonstrates how synthesis can clarify a clinically important question. In previous influenza pandemics, bacterial co-infections were a major cause of mortality. The authors systematically searched multiple databases for eligible studies published from January 2020 to April 2020 and included 30 studies with 3,834 patients. Overall, 7 percent of hospitalized COVID-19 patients had a bacterial co-infection, with a 95 percent confidence interval of 3 to 12 percent. A higher proportion of ICU patients had bacterial co-infections than patients in mixed ward and ICU settings, at 14 percent versus 4 percent. The pooled proportion with a viral co-infection was 3 percent. These findings did not support the routine use of antibiotics in the management of confirmed COVID-19 infection. Before this synthesis, individual case series and small studies had produced widely varying estimates of co-infection rates, leading to uncertainty about antibiotic stewardship.

Meta-analysis also helps resolve conflicts in the psychotherapy literature. Decades of research evidence and clinical experience converge on the conclusion that the psychotherapy relationship makes substantial and consistent contributions to outcome independent of the type of treatment. The Third Interdivisional American Psychological Association Task Force on Evidence-Based Relationships and Responsiveness summarized the meta-analytic results of 16 articles, each devoted to the link between a particular relationship element and treatment outcome. The expert consensus deemed 9 of the relationship elements as demonstrably effective, 7 as probably effective, and 1 as promising but with insufficient research to judge. Without quantitative synthesis, the field would remain divided between studies that emphasized technique and studies that emphasized relationship factors.

Identifying Moderators and Sources of Heterogeneity

A third purpose of meta-analysis is to identify moderators, which are variables that explain why the effect of an intervention or exposure differs across studies. Heterogeneity is the statistical term for variability in effect sizes across studies. When heterogeneity is present, a single pooled estimate may be misleading. Meta-analysis provides tools such as subgroup analysis and meta-regression to investigate the sources of that variability.

The meta-analysis of marine artificial reefs illustrates how synthesis can identify design and context factors that influence effectiveness. The analysis of 127 scientific papers characterized the design, objectives, and monitoring strategies used for artificial reefs. Statistical analyses using 67 variables allowed the authors to characterize the design, objectives, and monitoring strategies used for artificial reefs. An effectiveness indicator comprised of three categories, low, moderate, and high, was applied to the dataset in terms of the objectives defined in each scientific paper. The analyses showed that inert materials like concrete associated with biomimetic designs increase the benefits of reefs to the local environment. Fisheries projects showed the highest efficiencies, but the analysis also pointed out the weakness of environmental assessments for this type of project. The conclusion highlighted the need to use a panel of complementary monitoring techniques, independently of the initial purpose of the artificial structures, to properly assess the impact of such structures on the local environment.

The meta-analysis of invasive species legacy effects provides another example. Invasive species generate legacy effects that persist long after their removal, categorized into structural changes in landscape and geomorphology, biological changes in community composition and trophic interactions, and biogeochemical changes in nutrient cycling. The authors identified 46 studies suitable for quantitative meta-analysis, from which they extracted 649 paired observations. Biological legacies showed contrasting responses: removing invasive plants increased native plant performance in the long-term, had no net effect on species richness, and reduced native cover and biomass across both short- and long-term studies. Biogeochemical legacies were highly variable over time, showing significant nutrient increases in the long-term, whereas soil pH increased primarily in the short-term. The overall magnitude and duration of invasion legacies depended heavily on species identity, environmental context, and time elapsed since removal. These moderator effects would be invisible in any single study.

Improving Precision of Effect Estimates

Meta-analysis improves the precision of effect estimates by combining information across studies. The confidence interval around a pooled estimate is typically narrower than the confidence intervals around the individual study estimates. This increased precision has direct practical consequences. Clinicians can make more confident decisions about whether to prescribe a treatment. Policymakers can determine whether an intervention is cost-effective. Researchers can calculate more accurate sample sizes for future studies.

The systematic review and meta-analysis of arterial stiffness as a predictor of cardiovascular events and all-cause mortality demonstrates the value of precise estimates. The title of the work, "Prediction of cardiovascular events and all-cause mortality with arterial stiffness: a systematic review and meta-analysis," indicates the scope of the synthesis. By combining data from multiple prospective cohort studies, the authors were able to provide more precise estimates of the association between arterial stiffness and cardiovascular outcomes than any single cohort could provide. The same logic applies to the meta-analysis of hospital antimicrobial stewardship objectives, which synthesized evidence on the effectiveness of stewardship programs across multiple hospital settings.

Precision also matters for prevalence estimates. The umbrella review of ADHD prevalence provided a global estimate of 8.0 percent with a relatively narrow confidence interval of 6.0 to 10 percent. This estimate has implications for health service planning, resource allocation, and public health policy. A less precise estimate, such as one ranging from 2 to 20 percent, would be far less useful for decision-making.

Informing Evidence-Based Practice and Guidelines

Meta-analyses are among the most frequently cited forms of research because they provide the evidence base for clinical guidelines and policy decisions. Evidence-based guidelines do not recommend the use of inhaled or high-potency cannabis for medical purposes. High-potency cannabis compared with low-potency cannabis use is associated with increased risk of psychotic symptoms at 12.4 percent versus 7.1 percent and generalized anxiety disorder at 19.1 percent versus 11.6 percent. A meta-analysis of observational studies reported that 29 percent of individuals who used cannabis for medical purposes met criteria for cannabis use disorder. Daily inhaled cannabis use compared with nondaily use was associated with an increased risk of coronary heart disease at 2.0 percent versus 0.9 percent, myocardial infarction at 1.7 percent versus 1.3 percent, and stroke at 2.6 percent versus 1.0 percent. These findings, drawn from multiple meta-analyses, directly inform clinical guidance about the risks of cannabis use.

The clinical value of fecal calprotectin in inflammatory bowel disease provides another example of how meta-analytic evidence informs practice. Inflammatory bowel disease represents a growing global burden with a prevalence exceeding 0.3 percent in the Western world and an accelerating incidence in newly industrialized countries. The target for treating IBD has shifted from symptom control to mucosal healing, which has been shown to be associated with favorable long-term outcomes. The gold standard for ascertaining mucosal healing is endoscopic assessment, but endoscopy is limited by its invasive nature, high cost, and finite availability. Fecal calprotectin, a cytosolic protein derived predominantly from neutrophils, is now widely used as a surrogate biomarker. The review of the evidence supporting the use of fecal calprotectin in IBD, particularly as it pertains to screening, monitoring, and predicting disease relapse, synthesizes multiple studies to guide clinical use.

The Role of Meta-Analysis in Research Synthesis

Systematic reviews serve different purposes and use a different methodology than other types of evidence synthesis such as narrative reviews, scoping reviews, and overviews of reviews. A narrative review may summarize studies in a descriptive way, but it does not apply formal statistical methods to combine results. A scoping review may map the breadth of evidence on a topic without answering a specific question. A meta-analysis, by contrast, provides a quantitative answer to a specific question.

The steps of a successful systematic review include identification of an unanswered answerable question, explicit definitions of the investigation's participants, interventions, comparisons, and outcomes, utilization of PRISMA guidelines and PROSPERO registration, thorough systematic data extraction, and appropriate grading of the evidence and strength of the recommendations. The difference between meta-analyses and systematic reviews is that a systematic review may or may not include a meta-analysis. A meta-analysis is the statistical combination of results, while a systematic review is the entire process of identifying, evaluating, and synthesizing studies.

There are several tools that can guide and facilitate the systematic review process, but methodological and content expertise are always necessary. The EQUATOR Network provides reporting guidelines for health research, including PRISMA for systematic reviews and meta-analyses. The Experimental Design Assistant from NC3Rs helps researchers design rigorous animal experiments, which can improve the quality of primary studies that later feed into meta-analyses. The National Center for Biotechnology Information provides literature resources including PubMed, which is a primary database for searching biomedical literature. The Research Data Framework from the National Institute of Standards and Technology addresses data management practices that support reproducible research.

Practical Steps for Conducting a Meta-Analysis

Researchers who want to conduct a meta-analysis should follow a structured process. The first step is to formulate a clear research question. The question should specify the population, intervention or exposure, comparison, and outcomes. The question should be answerable, meaning that there is a sufficient body of primary studies to synthesize.

The second step is to develop a search strategy and search multiple databases. The umbrella review of ADHD prevalence systematically searched PubMed, Web of Science, PsychINFO, and Scopus to find pertinent studies. Searching multiple databases reduces the risk of missing relevant studies. The search should be documented in enough detail that another researcher could reproduce it.

The third step is to screen studies against explicit eligibility criteria. The criteria should be defined a priori, meaning before the search is conducted. This reduces the risk of bias in study selection. The umbrella review of ADHD prevalence included 13 meta-analytic systematic reviews with 588 primary studies and 3,277,590 participants in the final analysis.

The fourth step is to assess the quality or risk of bias of included studies. The umbrella review of ADHD prevalence used a Measurement Tool to Assess Systematic Reviews, known as AMSTAR, to assess the quality of the studies. Quality assessment is essential because the strength of a meta-analysis depends on the quality of the primary studies.

The fifth step is to extract data and calculate effect sizes. Each study must contribute an effect size and a measure of precision, such as a standard error or confidence interval. The meta-analysis of social cognition assessment tools extracted psychometric data including known-group, content and convergent validity, sensitivity and specificity, test-retest reliability, interrater reliability, and internal consistency from validation studies published until May 2024. Of 1,055 articles, 55 studies reporting psychometric data from 3,845 patients and 3,807 control subjects were included.

The sixth step is to conduct the statistical analysis. Pooled effect sizes, confidence intervals, and heterogeneity indices are estimated within a random-effect model. The choice between fixed-effect and random-effect models depends on assumptions about the underlying effect. A random-effect model assumes that the true effect varies across studies, while a fixed-effect model assumes a single true effect.

The seventh step is to assess publication bias and conduct sensitivity analyses. Publication bias occurs when studies with null or negative results are less likely to be published than studies with positive results. The meta-analysis of surgical publications found that assessment of publication bias was conducted in 26 of 31 meta-analyses, and assessment of statistical heterogeneity was conducted in 30 of 31. These quality assessments showed considerable variability even when reporting guidelines were observed.

Common Failure Patterns in Meta-Analysis

Meta-analyses can fail to provide reliable answers when they are conducted poorly. One common failure pattern is inadequate search strategy. If the search misses relevant studies, the pooled estimate will be biased. Searching only one database or restricting to English-language publications can introduce bias. The meta-analysis of surgical publications found that language of publication was exclusively English in 25 of 31 meta-analyses, which may limit generalizability.

A second failure pattern is inadequate quality assessment. The meta-analysis of surgical publications found that quality assessment of contributing publications was performed in only 10 of 31 meta-analyses. When the quality of primary studies is not assessed, the meta-analysis may combine strong and weak evidence without distinction. The strength of recommendations in a review is only as strong as the quality of studies that it analyzes.

A third failure pattern is ignoring heterogeneity. The meta-analysis of surgical publications found that assessment of statistical heterogeneity was conducted in 30 of 31 meta-analyses, but subgroup analysis was conducted in only 23 of 31. When heterogeneity is present but not explored, the pooled estimate may obscure important differences between subgroups. The meta-analysis of co-infections in COVID-19 found substantial heterogeneity, with an I-squared of 92.2 percent for bacterial co-infections. This heterogeneity was explained in part by the difference between ICU patients and mixed ward and ICU settings.

A fourth failure pattern is inadequate handling of missing data. The meta-analysis of surgical publications found that handling of missing data was addressed in only 10 of 31 meta-analyses. Missing outcome data can bias results if not handled appropriately.

A fifth failure pattern is overinterpretation of results. Meta-analyses are considered to provide level I to II evidence, but evidence derived from meta-analyses must be interpreted with caution. Although QUOROM guidelines were observed, quality assessments showed considerable variability. Readers and reviewers must recognize that the quality and strength of recommendations in a review are only as strong as the quality of studies that it analyzes.

Limitations and Caveats

Meta-analysis has important limitations that researchers must acknowledge. The most fundamental limitation is that a meta-analysis is only as good as the primary studies it includes. If the primary studies are biased, the pooled estimate will be biased. This is sometimes summarized as "garbage in, garbage out," though the principle is more formally described as the quality of the synthesis being limited by the quality of the evidence.

A second limitation is the problem of publication bias. Studies with null or negative results are less likely to be published than studies with positive results. If the meta-analysis only includes published studies, the pooled estimate may overstate the true effect. Statistical methods such as funnel plots and trim-and-fill analysis can detect publication bias, but they cannot fully correct for it.

A third limitation is the problem of heterogeneity. When studies are highly heterogeneous, the pooled estimate may not represent any actual population or setting. The meta-analysis of invasive species legacy effects found that the magnitude and duration of invasion legacies depend heavily on species identity, environmental context, and time elapsed since removal. A single pooled estimate would obscure these important differences.

A fourth limitation is the ecological fallacy. Associations observed at the study level may not hold at the individual level. Meta-analyses combine study-level data, not individual-level data. Individual-participant meta-analyses, which combine raw data from multiple studies, can address this limitation but are more resource-intensive. Examples include the meta-analysis of purpose in life and mortality with 488,765 participants followed for up to 32 years, the individual-participant meta-analysis of purpose in life and depressive symptoms with more than 500,000 participants across six world regions, and the individual-participant meta-analysis of purpose in life and grip strength with 115,972 participants from 24 countries across four continents.

A fifth limitation is the rapid growth of meta-analyses without corresponding growth in quality. The bibliometric analysis of meta-analyses on pediatric pain found that scientific production increased markedly after 2010, indicating growing emphasis on evidence synthesis in pediatric pain research. Publications were concentrated in a limited number of core journals, and authorship patterns reflected extensive international collaboration. Thematic analyses revealed a shift from intervention efficacy toward patient-centered outcomes, quality of life, and non-pharmacological approaches. However, growth in quantity does not guarantee growth in quality.

Quality Controls and Reporting Standards

Several tools and standards exist to improve the quality of systematic reviews and meta-analyses. The EQUATOR Network provides a comprehensive collection of reporting guidelines, including PRISMA for systematic reviews and meta-analyses. Following reporting guidelines does not guarantee a high-quality meta-analysis, but it does ensure that readers have the information they need to evaluate the methods.

The Experimental Design Assistant from NC3Rs helps researchers design rigorous animal experiments. Improving the quality of primary studies is one of the most effective ways to improve the quality of subsequent meta-analyses. The Research Data Framework from the National Institute of Standards and Technology addresses data management practices that support reproducible research. The National Center for Biotechnology Information provides literature resources including PubMed, which is a primary database for searching biomedical literature.

The umbrella review of ADHD prevalence was preregistered with PROSPERO under registration number CRD42023389704. Preregistration helps prevent selective reporting of outcomes and analyses. The quality of the studies was assessed using AMSTAR. These practices represent the current standard for rigorous evidence synthesis.

Professional Escalation Criteria

Researchers and clinicians should escalate concerns about a meta-analysis to a methodologist or statistician when they encounter certain warning signs. If the search strategy is not described in enough detail to be reproduced, the meta-analysis may not be reliable. If the quality assessment of included studies is not reported, the meta-analysis may combine strong and weak evidence without distinction. If heterogeneity is high but not explored through subgroup analysis or meta-regression, the pooled estimate may be misleading. If publication bias is not assessed, the pooled estimate may overstate the true effect.

Clinicians should be cautious about applying the results of a meta-analysis to an individual patient when the patient differs substantially from the populations included in the primary studies. The meta-analysis of social cognition assessment tools found robust outcomes for schizophrenia and autism spectrum disorder for theory of mind tests, but considerable heterogeneity and inconsistencies for other populations. Several tasks and batteries emerged as particularly well validated for specific conditions. The findings underscored the importance and possibility of selecting valid social attribution tests for a given population.

Policymakers should be cautious about basing decisions on a single meta-analysis. Convergent evidence from multiple independent meta-analyses is more reliable than any single synthesis. The umbrella review of ADHD prevalence was designed to provide a robust synthesis of evidence from systematic reviews and meta-analyses, recognizing that dozens of meta-analyses had already been conducted on the subject.

Frequently Asked Questions

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

A systematic review is a type of evidence synthesis in which authors develop explicit eligibility criteria, collect all the available studies that meet these criteria, and summarize results using reproducible methods that minimize biases and errors. A meta-analysis is the statistical combination of results from multiple studies. A systematic review may or may not include a meta-analysis. When studies are too heterogeneous in their designs, populations, or outcome measures, a narrative synthesis may be more appropriate than a quantitative meta-analysis.

Why is meta-analysis considered high-level evidence?

Meta-analyses are considered to provide level I to II evidence because they synthesize the results of multiple studies, often including randomized controlled trials. By combining data across studies, meta-analyses increase statistical power, improve precision, and can resolve conflicting findings. However, the quality of a meta-analysis depends on the quality of the primary studies it includes. Evidence derived from meta-analyses must be interpreted with caution, and quality assessments have shown considerable variability even when reporting guidelines are observed.

How does meta-analysis increase statistical power?

Individual studies are often underpowered to detect small but clinically meaningful effects. When multiple studies are combined, the pooled sample size is larger than any individual study. The umbrella review of ADHD prevalence included 13 meta-analytic systematic reviews with 588 primary studies and 3,277,590 participants, providing a global prevalence estimate of 8.0 percent with a 95 percent confidence interval of 6.0 to 10 percent. No single primary study could provide an estimate with this level of precision.

How does meta-analysis resolve conflicting findings across studies?

Meta-analysis provides a formal framework for determining whether conflicting results are compatible with a single underlying effect or whether they reflect genuine differences between studies. The systematic review and meta-analysis of co-infections in people with COVID-19 included 30 studies with 3,834 patients and found that 7 percent of hospitalized COVID-19 patients had a bacterial co-infection. Before this synthesis, individual case series and small studies had produced widely varying estimates, leading to uncertainty about antibiotic stewardship.

What is heterogeneity in meta-analysis and why does it matter?

Heterogeneity is the statistical term for variability in effect sizes across studies. When heterogeneity is present, a single pooled estimate may be misleading. Meta-analysis provides tools such as subgroup analysis and meta-regression to investigate the sources of variability. The meta-analysis of invasive species legacy effects found that the magnitude and duration of invasion legacies depend heavily on species identity, environmental context, and time elapsed since removal. These moderator effects would be invisible in any single study.

What is publication bias and how does it affect meta-analysis?

Publication bias occurs when studies with null or negative results are less likely to be published than studies with positive results. If the meta-analysis only includes published studies, the pooled estimate may overstate the true effect. Statistical methods such as funnel plots and trim-and-fill analysis can detect publication bias, but they cannot fully correct for it. The meta-analysis of surgical publications found that assessment of publication bias was conducted in 26 of 31 meta-analyses.

What is an umbrella review?

An umbrella review is a synthesis of existing systematic reviews and meta-analyses on a topic. The umbrella review of ADHD prevalence was designed to provide a robust synthesis of evidence from systematic reviews and meta-analyses. It included 13 meta-analytic systematic reviews with 588 primary studies and 3,277,590 participants. Umbrella reviews are useful when multiple meta-analyses have already been conducted and a higher-level synthesis is needed.

When should a meta-analysis not be performed?

A meta-analysis should not be performed when the included studies are too heterogeneous in their designs, populations, or outcome measures to justify combining them. A narrative synthesis may be more appropriate. A meta-analysis should also not be performed when the primary studies are of poor quality, because the pooled estimate will reflect the biases of the primary studies. The quality and strength of recommendations in a review are only as strong as the quality of studies that it analyzes.

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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.
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• Basics of meta-analysis.. Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology, 2020.
• Systematic reviews of the literature: an introduction to current methods.. American journal of epidemiology, 2025.
• Therapeutic Use of Cannabis and Cannabinoids: A Review.. JAMA, 2026.
• Psychotherapy relationships that work III.. Psychotherapy (Chicago, Ill.), 2018.
• How to write a systematic review.. The American journal of sports medicine, 2014.
• Meta-meta-analysis: A paradigm in the case of surgical publications.. Journal of B.U.ON. : official journal of the Balkan Union of Oncology, 2017.
• Clinical value of fecal calprotectin.. Critical reviews in clinical laboratory sciences, 2019.
• Global research trends and knowledge structure of meta-analyses on pediatric pain: A bibliometric analysis.. 2026.
• Meta-Analysis of Feature Representation Techniques for Fake News Detection: Evidence from Publicly Available Benchmark Datasets. 2026.
• Assessing Social Cognition in Neurological and Psychiatric Patients: A Psychometric Meta-analysis.. 2026.
• Leaving a trace: a meta-analysis of the legacy effects of invasive species. 2026.
• A Meta-Analysis. 2009.
• An examination of the efficacy of classical and Bayesian meta-analysis approaches for addressing important meta-analysis objectives. 2011.
• Marine artificial reefs, a meta-analysis of their design, objectives and effectiveness. Global Ecology and Conservation, 2021.
• Current evidence on hospital antimicrobial stewardship objectives: a systematic review and meta-analysis.. Lancet. Infectious Diseases (Print), 2016.
• Prediction of cardiovascular events and all-cause mortality with arterial stiffness: a systematic review and meta-analysis.. Journal of the American College of Cardiology, 2010.
• Co-infections in people with COVID-19: a systematic review and meta-analysis. Journal of Infection, 2020.
• Specialized Purpose of Each Type of Student Engagement: A Meta-Analysis. Educational Psychology Review, 2025.
• Purpose in life and mortality: A meta-analysis of the published literature and individual-participant data of 488,765 participants followed for up to 32 years. Psychological Medicine, 2026.
• Sense of meaning and purpose in life and risk of incident dementia: New data and meta-analysis. Archives of Gerontology and Geriatrics, 2023.
• Purpose in life and depressive symptoms: An individual-participant meta-analysis of >500,000 participants across six world regions. Journal of Affective Disorders, 2026.
• Purpose in life and grip strength: An individual-participant meta-analysis of 115,972 participants from 24 countries across four continents. Geroscience, 2025.
• Spiritual well-being, faith, meaning in life, peace, and purpose in life for cancer-related fatigue: systematic review with meta-analysis and meta-regressions. Journal of Cancer Survivorship, 2025.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.