Meta-Analysis Research Example: A Case Study in Life Sciences

Meta-analysis is a statistical approach that combines results from multiple independent studies to produce a pooled estimate of an effect or association. For students and researchers in the life sciences, examining a published meta-analysis provides the clearest path to understanding how these studies are designed, executed, and reported. This article walks through the structure of published meta-analyses using concrete examples from genetics, clinical medicine, and preclinical research. Each section highlights the research question, search strategy, data extraction, statistical methods, and reporting practices that define rigorous meta-analytic work.

What a Meta-Analysis Actually Does

A meta-analysis is not a literature review with statistics attached. It is a systematic process that begins with a focused research question, proceeds through a reproducible search and study selection protocol, extracts quantitative data from each eligible study, and then applies statistical models to combine those data. The goal is to increase statistical power, resolve conflicting findings across studies, and generate more precise estimates than any single study can provide.

The distinction between a systematic review and a meta-analysis matters. A systematic review identifies, evaluates, and synthesizes all available evidence on a question. A meta-analysis is the quantitative component that pools data from studies deemed sufficiently similar to combine. Some systematic reviews do not include a meta-analysis because the included studies are too heterogeneous or the data are not extractable. The EQUATOR Network maintains reporting guidelines that help readers distinguish between these approaches and evaluate whether authors followed accepted standards.

For life science researchers, the practical value of meta-analysis extends beyond clinical questions. Preclinical researchers use meta-analysis to inform clinical trial design and to explain discrepancies between animal study results and human trial outcomes. The Journal of Neuroscience Methods published a practical guide noting that preclinical studies are frequently small and often show substantial heterogeneity between studies, which affects both effect size calculation and data pooling methods. Basic science meta-analyses require adaptations to conventional techniques because experimental designs and models vary widely across laboratories.

Anatomy of a Published Meta-Analysis

Every published meta-analysis follows a recognizable structure. Understanding this structure allows readers to evaluate the quality of any meta-analysis they encounter and provides a template for designing new studies.

The Research Question and Protocol

A meta-analysis begins with a precisely defined question. The question must specify the population, intervention or exposure, comparator, and outcomes of interest. This framework, often called PICO, guides every subsequent decision in the review process.

The systematic review and network meta-analysis of community-based complex interventions to sustain independence in older people published in Health Technology Assessment provides a clear example. The authors defined their population as older people with a mean age of 65 or older living at home. The interventions were community-based complex services for sustaining independence. Comparators included usual care, placebo, or another complex intervention. Outcomes included living at home, activities of daily living, care-home placement, and service or economic outcomes at one year. This level of specificity allowed the authors to search systematically and apply consistent inclusion criteria.

Protocol registration is a hallmark of rigorous meta-analysis. The overview of reviews on physical activity and menopause symptoms published in BMC Women's Health registered its protocol on PROSPERO under registration number CRD42022298908. Prospective registration prevents duplication and allows readers to verify that the authors followed their stated methods instead of changing them after seeing the results.

The Search Strategy

A comprehensive search strategy is the foundation of a trustworthy meta-analysis. Authors must search multiple databases, use combinations of controlled vocabulary and free-text terms, and document their search strings so that other researchers can reproduce them.

The Cochrane review on endodontic procedures for retreatment of periapical lesions illustrates thorough searching. The authors searched the Cochrane Oral Health Trials Register, CENTRAL, MEDLINE Ovid from 1946, and Embase Ovid from 1980. They also searched ClinicalTrials.gov and the WHO International Clinical Trials Registry Platform for ongoing trials. No restrictions were placed on language or publication date, and the authors handsearched reference lists of retrieved studies and key journals in endodontics.

The systematic review and meta-analysis on teriparatide for medication-related osteonecrosis of the jaw demonstrates a focused search approach. The authors searched three databases, PubMed, Embase, and Cochrane CENTRAL, using the terms "Teriparatide" OR "TPTD" OR "Recombinant Parathyroid Hormone" OR "Recombinant PTH" combined with "Medication-Related Osteonecrosis of the Jaw" OR "MRONJ" OR "BRONJ". No publication date range was applied. This search identified 162 studies, of which only 5 met the inclusion criteria after screening.

Study Selection and Inclusion Criteria

Inclusion and exclusion criteria must be established before screening begins. These criteria operationalize the research question and determine which studies contribute data to the analysis.

The teriparatide meta-analysis provides a clear example of explicit criteria. The authors included randomized controlled trials, case-control studies, and cohort studies. They excluded systematic reviews, case reports, case series, animal studies, editorials, non-English publications, and studies relating to osteoradionecrosis. Each study was screened independently by multiple reviewers, with final selection determined after discussion among all authors.

The systematic review protocol for embolic complications of cardiac myxoma shows how screening is structured in a prospective protocol. Six reviewers will perform title and abstract screening and full-text screening in pairs across three screening groups. Disagreements will be resolved through discussion, with senior reviewer adjudication where necessary. Data will be extracted by three reviewers and independently verified for accuracy, with discrepancies resolved through discussion or third-party adjudication.

Data Extraction

Data extraction converts information from each included study into a standardized format suitable for analysis. This process requires careful attention to outcome definitions, measurement units, and statistical reporting.

The Global Burden of Disease Study analysis of osteoarthritis published in The Lancet Rheumatology demonstrates how data extraction handles definitional variation across studies. The reference case definition was symptomatic, radiographically confirmed osteoarthritis. Studies using alternative definitions, such as self-reported osteoarthritis, were adjusted to the reference definition using regression models. This adjustment allowed the authors to combine data from population-based surveys across 26 countries for knee osteoarthritis, 23 countries for hip osteoarthritis, and 42 countries for hand osteoarthritis.

The three-level meta-analysis of social problem-solving interventions for autism illustrates the complexity of extracting data from single-case experimental designs. The authors extracted 114 dependent-variable-level effect sizes nested within 59 participant clusters from 21 studies published between 2002 and 2025. This nested data structure required a three-level meta-analytic model to account for the dependency among effect sizes from the same participants.

Statistical Methods for Pooling

The choice of statistical model depends on the nature of the data and the expected heterogeneity among studies. Fixed-effect models assume that all studies estimate the same underlying effect. Random-effects models allow for variation between studies and are generally preferred when heterogeneity is expected.

The multi-ancestry meta-analysis of genome-wide association studies for PTSD published in Nature Genetics pooled data across 1,222,882 individuals of European ancestry with 137,136 cases and 58,051 admixed individuals with African and Native American ancestry with 13,624 cases. This massive pooling required sophisticated statistical methods to account for population structure and ancestry differences while identifying 95 genome-wide significant loci, 80 of which were new.

The network meta-analysis of community-based interventions for older people used a random-effects network meta-analysis approach. This method allows comparison of multiple interventions simultaneously, even when direct head-to-head trials do not exist. The authors identified 19 intervention components in 63 combinations across 129 studies with 74,946 participants. They assessed trial-result risk of bias using the Cochrane Risk of Bias 2 tool, evaluated network meta-analysis inconsistency, and graded certainty of evidence using the GRADE framework adapted for network meta-analysis.

Heterogeneity and Sensitivity Analysis

Heterogeneity refers to variability in study results beyond what would be expected by chance alone. Quantifying and exploring heterogeneity is essential because high heterogeneity undermines confidence in pooled estimates.

The meta-analysis of APOE genetic effects on Alzheimer's disease risk in the Japanese population demonstrates how meta-analysis can resolve conflicting findings. The authors found that the risk effect of the APOE-e44 genotype relative to APOE-e33 for Alzheimer's disease in the Japanese population is approximately 12 to 15-fold, comparable to that reported in Caucasian populations, instead of greater than 20-fold as previously reported. This finding corrected a discrepancy that had persisted in the literature.

The three-level meta-analysis of social problem-solving interventions reported low approximate total heterogeneity with an I-squared value of 24.48 percent. Moderator analyses did not identify clear statistically significant predictors of effect-size variability across intervention centrality, setting, type, implementer, participant characteristics, or methodological quality. Sensitivity and publication-bias analyses provided no clear indication that findings were driven by a single influential study or by obvious small-study effects.

At a Glance

Meta-Analysis in Genetic Association Studies

Genetic association studies present unique challenges and opportunities for meta-analysis. Individual studies often lack statistical power to detect variants with small effects. Combining data across studies increases power and enables discovery that no single study could achieve.

Multi-Ancestry Meta-Analysis

The genome-wide association study of rheumatoid arthritis published in Nature Genetics demonstrates the power of multi-ancestry meta-analysis. The authors included 276,020 samples from five ancestral groups and identified 124 loci at genome-wide significance, of which 34 were novel. Multi-ancestry fine-mapping identified putatively causal variants with biological insights, including LEF1. The polygenic risk scores based on multi-ancestry GWAS outperformed scores based on single-ancestry GWAS and showed comparable performance between European and East Asian populations.

The PTSD genetics study similarly used multi-ancestry meta-analysis to overcome the lower discoverability that characterizes PTSD genetics compared to other psychiatric disorders. Convergent multi-omic approaches identified 43 potential causal genes, broadly classified as neurotransmitter and ion channel synaptic modulators, developmental and axon guidance transcription factors, synaptic structure and function genes, and endocrine or immune regulators.

Meta-Analysis of Polygenic Risk Scores

The study of preeclampsia and gestational hypertension published in Nature Medicine illustrates how meta-analysis supports polygenic risk score development. The authors tested maternal DNA sequence variants in 20,064 preeclampsia cases and 703,117 controls, and in 11,027 gestational hypertension cases and 412,788 controls, across discovery and follow-up cohorts using multi-ancestry meta-analysis. They identified 18 independent loci, 12 of which were new. The derived genome-wide polygenic risk scores predicted both conditions in external cohorts independent of clinical risk factors and reclassified eligibility for low-dose aspirin to prevent preeclampsia.

Meta-Analysis in Disease Burden Estimation

The Global Burden of Disease Study analysis of osteoarthritis shows how meta-analysis contributes to population health estimation. Osteoarthritis is the most common form of arthritis in adults, characterized by chronic pain and loss of mobility. It most frequently occurs after age 40 and prevalence increases steeply with age. The World Health Organization designated 2021 to 2030 as the decade of healthy ageing, highlighting the need to address diseases that strongly affect functional ability and quality of life.

The authors estimated osteoarthritis prevalence in 204 countries and territories from 1990 to 2020. They obtained the osteoarthritis severity distribution from a pooled meta-analysis of sources using the Western Ontario and McMaster Universities Arthritis Index. Final prevalence estimates were multiplied by disability weights to calculate years lived with disability. Globally, 595 million people had osteoarthritis in 2020, equal to 7.6 percent of the global population. Prevalence was forecast to 2050 using a mixed-effects model.

Meta-Analysis in Clinical Intervention Research

Clinical meta-analyses synthesize evidence from randomized controlled trials to guide treatment decisions. These analyses must address issues of trial quality, outcome definition, and clinical heterogeneity.

Exercise Interventions for Heart Failure

The systematic review and meta-analysis of exercise effects on aerobic capacity and quality of life in heart failure patients published in Applied Sciences demonstrates the use of meta-analysis to identify optimal treatment parameters. The authors searched Embase, PubMed, Cochrane Library, Web of Science, and Scopus through October 2024. They used the PICO framework to define inclusion criteria and assessed study quality using the Physiotherapy Evidence Database scale and the Cochrane Risk of Bias 2 tool.

Forty-seven studies met the inclusion criteria. Exercise significantly improved aerobic capacity with a weighted mean difference of 2.85 and quality of life with a standardized mean difference of negative 0.79. Subgroup analyses indicated that combined exercise, session duration of at least 60 minutes, at least three sessions per week, at least 180 minutes per week, and supervised exercise showed more significant improvements. The optimal prescription involved supervised combined exercise at least three times per week.

Physical Activity for Menopause Symptoms

The overview of reviews on physical activity and menopause symptoms published in BMC Women's Health illustrates a synthesis approach when meta-analysis is not appropriate. The authors searched Medline, Embase, CINAHL, Scopus, the Cochrane Database of Systematic Reviews, and Social Science Citation Index in June 2023. They assessed reviews using AMSTAR-2 and adopted a best-evidence approach to synthesis without meta-analysis.

Seventeen reviews included 80 unique relevant primary studies with 8,983 participants. Evidence showed improvement of physical, urogenital, and total symptoms following yoga interventions. Evidence for vasomotor and psychological symptoms was inconclusive. Findings for aerobic exercise were inconclusive, although some examples showed beneficial effects on total and vasomotor symptoms. The authors concluded there is some evidence that yoga and, to a lesser extent, aerobic exercise may benefit some menopause symptoms, but insufficient evidence exists to recommend a particular form of exercise.

Teriparatide for Medication-Related Osteonecrosis of the Jaw

The systematic review and meta-analysis of teriparatide for medication-related osteonecrosis of the jaw demonstrates advanced statistical methods for pooling survival data. The primary meta-analysis used Cox proportional hazards regression with pseudo-individual-subject data reconstructed from published Kaplan-Meier curves using the Guyot method. A sensitivity analysis pooled odds ratios at the 6-month clinical response endpoint.

After review of 162 studies, only 5 met the inclusion criteria. This low inclusion rate highlights the reality that many published studies fail to meet rigorous methodological standards. The authors reported primary outcomes including improvement in clinical staging and secondary outcomes including time to improvement, adverse effects, radiographic evaluation, and serum markers.

Meta-Analysis in Preclinical and Basic Research

Meta-analysis is less common in basic life science research than in clinical research, but it offers substantial benefits for hypothesis generation and experimental design.

Adapting Methods for Preclinical Data

The practical guide for meta-analysis of animal studies published in the Journal of Neuroscience Methods addresses the unique challenges of preclinical data. Preclinical studies are frequently small and often show substantial heterogeneity between studies. These characteristics affect both the method of calculating effect sizes and the method of pooling data. The guide describes methods used to explore sources of heterogeneity, which is essential because animal studies vary widely in species, strain, sex, age, housing conditions, and experimental protocols.

The Meta-Analytic Methodology for Basic Research published in Frontiers in Physiology extends conventional meta-analytic techniques to accommodate basic research practices. The authors introduced MetaLab, a meta-analytic toolbox developed in MATLAB R2016b. They demonstrated the workflow using a rapid review of intracellular ATP concentrations in osteoblasts and a systematic review of mechanically-stimulated ATP release kinetics in mammalian cells. The authors emphasized criteria required to ensure outcome validity and exploratory methods to identify influential experimental and biological factors.

Meta-Analysis of Single-Case Experimental Designs

The three-level meta-analysis of social problem-solving interventions for autism demonstrates how meta-analytic methods accommodate non-traditional study designs. Single-case experimental designs present unique statistical challenges because multiple effect sizes are nested within participants and studies. The three-level model accounted for this dependency structure.

The unconditional three-level model showed a positive and statistically significant pooled effect of 1.741 with a standard error of 0.087 and a 95 percent confidence interval from 1.570 to 1.911. The authors noted that because many studies used multi-component intervention packages, the observed effects reflect the combined impact of interventions in which social problem-solving components were embedded. It was not possible to isolate the independent contribution of social problem-solving components based on the available evidence.

Meta-Analysis in Environmental Science

The study on vulnerability of marine biodiversity to ocean acidification published in Estuarine Coastal and Shelf Science demonstrates the application of meta-analysis to ecological questions. This work shows that meta-analytic methods are not limited to biomedical research but can synthesize experimental data across species and ecosystems to identify general patterns of response to environmental stressors.

Practical Steps for Conducting a Meta-Analysis

Researchers planning a meta-analysis should follow a structured workflow that mirrors the practices of published exemplars.

Step 1: Define the Question and Register the Protocol

Write the research question using the PICO framework. Specify the population, intervention or exposure, comparator, and primary and secondary outcomes. Register the protocol prospectively in a registry such as PROSPERO. The overview of reviews on menopause symptoms provides an example of protocol registration with a clear registration number.

Step 2: Develop and Execute the Search Strategy

Identify the databases relevant to your field. For biomedical research, this typically includes MEDLINE, Embase, and CENTRAL. For broader life science questions, add Scopus, Web of Science, and field-specific databases. Document the exact search strings, date ranges, and any language restrictions. The Cochrane review on endodontic retreatment demonstrates comprehensive searching across multiple databases and trial registries.

Step 3: Screen Studies and Extract Data

Establish inclusion and exclusion criteria before screening begins. Use multiple independent reviewers with a process for resolving disagreements. Extract data into a standardized form that captures study characteristics, participant demographics, intervention details, outcome measures, and effect estimates. The cardiac myxoma protocol describes a rigorous screening and extraction process with independent verification.

Step 4: Assess Risk of Bias and Study Quality

Use validated tools appropriate to the study designs included. The network meta-analysis of community interventions used the Cochrane Risk of Bias 2 tool for trial results. The exercise and heart failure meta-analysis used both the Physiotherapy Evidence Database scale and the Cochrane Risk of Bias 2 tool.

Step 5: Perform Statistical Analysis

Choose between fixed-effect and random-effects models based on expected heterogeneity. For preclinical data, consider the adaptations described in the Journal of Neuroscience Methods guide. For complex data structures, consider advanced approaches such as the three-level model used in the social problem-solving meta-analysis or the network meta-analysis approach used in the community interventions study.

Step 6: Explore Heterogeneity and Conduct Sensitivity Analyses

Quantify heterogeneity using appropriate statistics and explore potential sources through subgroup and moderator analyses. Conduct sensitivity analyses to determine whether results are robust to decisions about study inclusion, statistical methods, and outcome definitions. The APOE meta-analysis demonstrates how meta-analysis can correct erroneous findings when heterogeneity is properly explored.

Step 7: Report According to Established Guidelines

Follow the reporting guidelines maintained by the EQUATOR Network. These guidelines ensure that readers can evaluate the methods and replicate the analysis. The cardiac myxoma protocol explicitly states adherence to PRISMA-P guidelines for systematic review protocols.

Records and Measurements in Meta-Analysis

Maintaining detailed records throughout a meta-analysis is essential for reproducibility and transparency.

Documentation Requirements

Document every search string, database, and search date. Record the number of studies identified at each stage of screening, typically presented in a PRISMA flow diagram. Document reasons for exclusion at the full-text stage. Record data extraction forms and any decisions made during the extraction process. The teriparatide meta-analysis reported that 162 studies were reviewed and only 5 were included, demonstrating the importance of transparent reporting of the screening funnel.

Data Management

Use standardized data extraction forms that capture all variables needed for analysis. For genetic studies, document ancestry information, genotyping platforms, and quality control procedures. The rheumatoid arthritis study included 276,020 samples from five ancestral groups, requiring careful documentation of ancestry assignment and quality control.

Statistical Software and Reproducibility

Document the statistical software and version used for analysis. For basic research meta-analyses, tools such as MetaLab provide standardized workflows. The Frontiers in Physiology methodology provides computational resources and a workflow outline that researchers can adapt for their own analyses.

Common Failure Patterns in Meta-Analysis

Understanding how meta-analyses fail helps researchers avoid these pitfalls and helps readers evaluate published work critically.

Inadequate Search Strategies

A meta-analysis that searches only one or two databases will miss relevant studies. The Cochrane review on endodontic retreatment searched five databases plus trial registries and handsearched reference lists. Failure to search trial registries misses unpublished or ongoing studies, which can introduce publication bias.

Ignoring Heterogeneity

Pooling studies that are too different produces misleading estimates. The Journal of Neuroscience Methods guide emphasizes that substantial heterogeneity between preclinical studies affects both effect size calculation and pooling methods. Researchers must explore sources of heterogeneity instead of ignoring it.

Inappropriate Statistical Models

Using a fixed-effect model when substantial heterogeneity exists produces overly narrow confidence intervals and misleading precision. The network meta-analysis of community interventions used a random-effects model appropriate for the expected variation across 129 studies with diverse intervention components.

Poor Reporting

Incomplete reporting prevents readers from evaluating the validity of a meta-analysis. The EQUATOR Network maintains reporting guidelines that address this problem. Authors should follow these guidelines and readers should check for adherence before accepting conclusions.

Overinterpretation of Results

Meta-analyses cannot overcome the limitations of the included studies. The social problem-solving meta-analysis noted that because many studies used multi-component intervention packages, it was not possible to isolate the independent contribution of social problem-solving components. Authors must acknowledge such limitations instead of overstating their conclusions.

Limitations and Professional Escalation Criteria

Meta-analysis has inherent limitations that researchers and readers must recognize.

Limitations of Meta-Analysis

Meta-analysis cannot correct for poor quality in the included studies. If the primary studies have high risk of bias, the pooled estimate will reflect that bias. The teriparatide meta-analysis found only 5 eligible studies from 162 initially identified, indicating that most published research on this question did not meet methodological standards.

Publication bias occurs when studies with null or negative results are less likely to be published. Meta-analyses that include only published studies may overestimate effects. The social problem-solving meta-analysis conducted publication-bias analyses and found no clear indication of small-study effects, but this is not always the case.

Meta-analysis cannot resolve fundamental problems with the underlying evidence base. If the primary studies use inconsistent outcome definitions, the pooled estimate may not be meaningful. The Global Burden of Disease osteoarthritis analysis addressed this by adjusting studies using alternative definitions to the reference case definition using regression models.

When to Seek Professional Statistical Support

Researchers should seek professional statistical support when planning a meta-analysis that involves complex data structures, such as nested effect sizes, network comparisons, or reconstruction of individual patient data from published curves. The teriparatide meta-analysis used the Guyot method to reconstruct pseudo-individual-subject data from published Kaplan-Meier curves, a technique that requires specialized statistical expertise.

Researchers should also seek support when dealing with multi-ancestry genetic data. The PTSD genetics study and the rheumatoid arthritis study required sophisticated methods to account for population structure and ancestry differences.

Escalation Criteria for Methodological Concerns

Readers evaluating a published meta-analysis should escalate concerns to the journal or seek expert consultation when they identify serious methodological problems. These include failure to register the protocol, incomplete search strategies, inappropriate statistical models, unexplained heterogeneity, or conclusions that exceed the evidence.

Safety and Regulatory Context

Meta-analyses in the life sciences operate within regulatory and ethical frameworks that researchers must understand.

Clinical Research Context

Clinical meta-analyses inform treatment decisions and guideline development. The exercise and heart failure meta-analysis provides evidence for exercise prescription in heart failure patients. The menopause symptoms overview addresses the need for non-pharmacological options for women who cannot or do not want to take hormone replacement therapy.

Genetic Research Context

Genetic meta-analyses raise privacy and ethical considerations. Studies involving large numbers of participants, such as the preeclampsia study with over 700,000 controls, must comply with data protection regulations and informed consent requirements. The Research Data Framework from the National Institute of Standards and Technology provides guidance on data management practices that support reproducibility and responsible data sharing.

Preclinical Research Context

Preclinical meta-analyses should follow animal research reporting standards. The Experimental Design Assistant from the NC3Rs provides tools for designing experiments that minimize bias and improve reproducibility. Meta-analyses of animal studies can inform clinical trial design and help explain discrepancies between preclinical and clinical results, as described in the Journal of Neuroscience Methods guide.

Frequently Asked Questions

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

A systematic review is a comprehensive, reproducible process for identifying, evaluating, and synthesizing all available evidence on a research question. A meta-analysis is the statistical component that pools quantitative data from studies deemed sufficiently similar to combine. Some systematic reviews do not include a meta-analysis because the included studies are too heterogeneous or the data cannot be extracted. The EQUATOR Network maintains reporting guidelines that help distinguish between these approaches.

How do researchers decide whether to use a fixed-effect or random-effects model?

A fixed-effect model assumes that all studies estimate the same underlying effect and that variation between studies is due only to sampling error. A random-effects model allows for genuine variation between studies and is generally preferred when heterogeneity is expected. The network meta-analysis of community interventions for older people used a random-effects model because the 129 included studies varied substantially in intervention components and populations.

How does meta-analysis handle studies with different outcome definitions?

Researchers can adjust results from studies using alternative definitions to a reference definition using regression models. The Global Burden of Disease osteoarthritis analysis adjusted studies using self-reported osteoarthritis to the reference case definition of symptomatic, radiographically confirmed osteoarthritis. Alternatively, researchers can conduct subgroup analyses or sensitivity analyses to examine whether results differ by outcome definition.

What is publication bias and how do meta-analysts detect it?

Publication bias occurs when studies with null or negative results are less likely to be published than studies with positive results. Meta-analyses that include only published studies may therefore overestimate effects. The social problem-solving meta-analysis conducted publication-bias analyses and found no clear indication of small-study effects, but this is not always the case. Researchers can also search trial registries to identify unpublished studies.

Can meta-analysis be used for animal studies and basic research?

Yes, but the methods require adaptation. Preclinical studies are frequently small and show substantial heterogeneity between studies, which affects both effect size calculation and pooling methods. The Journal of Neuroscience Methods guide provides practical guidance for meta-analysis of animal data. The Frontiers in Physiology methodology extends conventional techniques to accommodate basic research practices.

How do multi-ancestry meta-analyses differ from single-ancestry analyses?

Multi-ancestry meta-analyses combine data from multiple ancestral groups, which improves power to detect genetic signals, improves fine-mapping resolution, and improves the performance of polygenic risk scores. The rheumatoid arthritis study found that polygenic risk scores based on multi-ancestry GWAS outperformed scores based on single-ancestry GWAS and showed comparable performance between European and East Asian populations.

What is a network meta-analysis?

A network meta-analysis allows comparison of multiple interventions simultaneously, even when direct head-to-head trials do not exist. The network meta-analysis of community-based interventions for older people identified 19 intervention components in 63 combinations across 129 studies. This approach allows indirect comparisons between interventions that have not been directly compared in trials.

How should researchers report a meta-analysis?

Researchers should follow the reporting guidelines maintained by the EQUATOR Network. These guidelines specify what information must be reported to allow readers to evaluate the methods and replicate the analysis. The cardiac myxoma protocol explicitly states adherence to PRISMA-P guidelines for systematic review protocols.

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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.
• Global, regional, and national burden of osteoarthritis, 1990-2020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021.. The Lancet. Rheumatology, 2023.
• Polygenic prediction of preeclampsia and gestational hypertension.. Nature medicine, 2023.
• Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder.. Nature genetics, 2024.
• Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis.. Nature genetics, 2022.
• The impact of physical activity and exercise interventions on symptoms for women experiencing menopause: overview of reviews.. BMC women's health, 2024.
• Community-based complex interventions to sustain independence in older people, stratified by frailty: a systematic review and network meta-analysis.. Health technology assessment (Winchester, England), 2024.
• Antibiotic use in endodontic treatment during pregnancy: A narrative review.. European journal of translational myology, 2022.
• Endodontic procedures for retreatment of periapical lesions.. The Cochrane database of systematic reviews, 2016.
• A reappraisal of APOE genetic effects on Alzheimer's disease risk in the Japanese population: a meta-analysis.. 2026.
• Single-Case Experimental Evidence on Social Problem-Solving Interventions for Individuals with Autism: A Three-Level Meta-Analysis.. 2026.
• Should Teriparatide Be Part of Our Clinical Armamentarium in the Management of Medication-Related Osteonecrosis of the Jaw? A Systematic Review and Meta-Analysis.. 2026.
• Frequency, Clinical Characteristics, Anatomical Distribution, and Outcomes of Embolic Complications of Cardiac Myxoma in Contemporary Cohorts: Protocol for a Systematic Review and Meta-Analysis.. 2026.
• Meta-analysis of data from animal studies: a practical guide.. Journal of Neuroscience Methods, 2014.
• Meta-analysis of data from animal studies. 2013.
• Meta-Analytic Methodology for Basic Research: A Practical Guide. Frontiers in Physiology, 2019.
• Understanding, manipulating and owning life. Meta Science, 2022.
• Journal of Neuroscience Methods
• Effects of Exercise on Aerobic Capacity and Quality of Life in People with Heart Failure: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Applied Sciences, 2025.
• Bayesian meta-analysis: A practical introduction. Bayesian Meta Analysis A Practical Introduction, 2025.
• What meta-analysis can tell us about vulnerability of marine biodiversity to ocean acidification?. Estuarine Coastal and Shelf Science, 2010.

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