Meta-Analysis Research Design: Key Considerations and Methodological Choices

A meta-analysis is a quantitative synthesis of information from several studies, and its value depends entirely on the design decisions made before any statistical pooling begins. For researchers planning a meta-analysis, the core task is to build a protocol that specifies the research question, the search strategy, the inclusion criteria, and the statistical methods before data collection starts. This article walks through each design decision with concrete steps, common errors, and the records you should keep along the way.

Systematic reviews apply scientific methods to reduce bias when reviewing literature, and the key components include a well-defined research question, a comprehensive literature search, systematic assembly of studies, critical appraisal of methodological quality, data extraction and analysis, and consideration of how the evidence applies to practice [6]. Meta-analysis is the statistical tool that provides pooled estimates of effect from the data extracted from individual studies in a systematic review [6]. The graphical output of a meta-analysis is a forest plot, which provides information on individual studies and the pooled effect [6].

Because meta-analysis is a retrospective research design in most cases, it is subject to a variety of selection biases that can undermine its validity [7]. A major challenge is to differentiate genuine between-study heterogeneity from systematic errors and biases [7]. The design choices you make at the protocol stage determine whether your results can withstand scrutiny.

At a Glance: Core Design Decisions

Defining the Research Question

The research question is the foundation of every design decision that follows. A well-defined question specifies the population, the intervention or exposure, the comparator, and the outcomes of interest. This structure is often called the PICO framework, and it forces you to make explicit choices that guide your search terms and inclusion criteria.

For example, a question about daily steps and mortality might specify adults aged 18 years and older, device-measured daily steps as the exposure, and all-cause mortality as the outcome [20]. A question about high-intensity interval training might specify healthy athletes, specific protocol structures, and accumulated time at or above 90 percent of maximal oxygen uptake as the outcome [14]. The precision of these definitions determines whether your search strategy can identify all relevant studies.

The research question also determines the type of studies you will include. Systematic reviews can be undertaken for all types of questions and all types of study designs [6]. A question about intervention effectiveness might focus on randomized controlled trials, while a question about prevalence or risk factors might include cohort and cross-sectional studies. The hierarchy of research designs places randomized controlled trials at the highest grade, but observational studies can provide valid evidence when randomized trials are not feasible or ethical [12].

When you define the research question, write it in a form that can be answered by the studies you expect to find. If the question requires data that most studies do not report, you will face missing data problems at the extraction stage. If the question is too broad, you will include studies that are too diverse to synthesize meaningfully.

Protocol Development and Registration

A protocol is a written plan that specifies every step of the meta-analysis before you begin. The protocol should state the research question, the search strategy, the inclusion and exclusion criteria, the data extraction process, the quality appraisal method, and the statistical analysis plan. This document serves as a record of your decisions and helps prevent post hoc changes that could introduce bias.

The PRISMA protocol is often used in conducting meta-analysis [16]. The PRISMA checklist and flow diagram provide a structure for reporting how studies were identified, screened, and included [16]. Reporting guidelines are available through the EQUATOR Network, which maintains a collection of reporting standards for health research [2]. Following a reporting guideline does not guarantee a valid meta-analysis, but it ensures that readers can see what you did.

Registration of the protocol in a public registry is a common practice that helps prevent duplication and allows readers to compare your planned methods with what you actually did. The protocol should include the planned search dates, the databases to be searched, and the methods for handling studies that do not report the data you need.

The protocol also serves a practical purpose during the review process. When you encounter a study that does not fit your initial criteria, the protocol tells you whether to include it, exclude it, or document a protocol amendment. Without a protocol, decisions made during the review process can reflect your knowledge of the results, which undermines the objectivity of the synthesis.

Search Strategy Design

The search strategy determines which studies you will find and therefore which studies you can include. A comprehensive search covers multiple databases, uses a combination of subject headings and free-text terms, and documents the exact search strings used for each database.

The National Center for Biotechnology Information provides access to literature databases including PubMed, which is a primary resource for biomedical literature [4][5]. Searching only one database risks missing relevant studies, and the choice of databases should reflect the topic area. A meta-analysis on daily steps and health outcomes searched PubMed and EBSCO CINAHL, supplemented by other search strategies [17]. A protocol for a review on large language models in ovarian cancer management planned searches across biomedical, technical, and Chinese-language databases including PubMed, Embase, Web of Science, IEEE Xplore, and China National Knowledge Infrastructure [15].

The search strategy should be developed iteratively. Start with a small set of known relevant studies and test whether your search terms find them. Then expand the search terms to capture variations in terminology across studies and over time. Record the date each search was run, because search results change as new studies are published.

The search strategy should not be limited by language or publication type without justification. A review on daily steps and health outcomes explicitly included studies without restrictions on language or publication type [17]. Restricting by language can introduce bias if studies with certain results are more likely to be published in a particular language.

Inclusion and Exclusion Criteria

Inclusion criteria specify the characteristics that studies must have to be included in the meta-analysis. These criteria operationalize the research question and should be applied consistently to all studies identified by the search.

The criteria should specify the study design, the population, the intervention or exposure, the comparator, and the outcome measures. For example, a meta-analysis on daily steps and all-cause mortality included prospective studies that examined the relationship between device-measured daily steps and health outcomes among adults [17]. A network meta-analysis on high-intensity interval training included controlled studies reporting accumulated time at or above 90 percent of maximal oxygen uptake in healthy athletes [14].

Inclusion criteria also need to address the measurement of key variables. In a review of sugar-sweetened beverages and type 2 diabetes, the researchers found that across 11 studies, 7 measured diet only once, 5 included primarily low consumers of sugar-sweetened beverages, and 3 defined the study variable as consumption of either sugar or artificially sweetened beverages [11]. These differences in measurement would have been invisible without explicit criteria for how the exposure was defined.

The inclusion criteria should be tested against a sample of studies before the full screening process begins. This pilot test helps identify criteria that are too vague to apply consistently or too narrow to capture the relevant literature. The screening process should involve two reviewers working independently, with disagreements resolved through discussion or a third reviewer [15].

Data Extraction and Management

Data extraction is the process of collecting the information you need from each included study. The extraction form should be designed before screening begins and pilot-tested on a small number of studies to ensure that all relevant data can be captured.

The extraction form should include study characteristics such as the design, sample size, setting, and follow-up duration. It should also include the data needed for effect size calculation, such as means, standard deviations, event counts, and hazard ratios. For studies that report results in different formats, the extraction form should include space for the raw data needed to convert results to a common metric.

Two independent reviewers should perform data extraction, with disagreements resolved through discussion [15]. The agreement between reviewers should be recorded, because it provides evidence of the reliability of the extraction process. If reviewers frequently disagree, the extraction form may need to be revised.

Missing data is a common problem in meta-analysis. Studies may not report the statistics you need, or they may report them in a format that cannot be converted to your chosen effect size. The protocol should specify how missing data will be handled, such as contacting study authors, imputing missing values from other statistics, or excluding studies from the quantitative synthesis.

The data management process should include a system for tracking which studies have been screened, which have been included, and which have been excluded with reasons. The PRISMA flow diagram provides a structure for reporting this information [16]. The flow diagram shows the number of records identified, the number screened, the number excluded, and the number included in the synthesis.

Quality Appraisal of Included Studies

Quality appraisal is the critical assessment of the methodological quality of the included studies. This step is essential because the validity of a meta-analysis depends on the validity of the individual studies.

The choice of appraisal tool depends on the study designs included. For randomized trials, the Cochrane risk-of-bias tool is commonly used [15]. For nonrandomized studies, tools such as the Risk of Bias in Nonrandomized Studies of Interventions are available [15]. For observational studies, the Newcastle-Ottawa Scale is a common choice, and it was used in a review on daily steps and health outcomes [17].

Quality appraisal should be conducted independently by two reviewers, with disagreements resolved through discussion. The results of the appraisal should be reported for each included study, and the protocol should specify how quality will be incorporated into the synthesis. Options include excluding low-quality studies in sensitivity analyses, weighting studies by quality, or examining quality as a moderator.

The appraisal results should be presented in a way that allows readers to see the strengths and weaknesses of the evidence base. A table showing the quality rating for each study across each domain is a common approach. This table also helps readers understand how much confidence to place in the pooled estimates.

Effect Size Calculation and Common Metrics

Meta-analysis requires that all effect sizes be transformed into a common metric before they can be combined [8]. The choice of effect size metric depends on the type of data and the research question.

For continuous outcomes, the standardized mean difference is commonly used when studies measure the same outcome with different scales. For binary outcomes, odds ratios, risk ratios, or risk differences are used. For time-to-event outcomes, hazard ratios are used. For prevalence data, the prevalence proportion is the effect size.

When a meta-analysis includes results from different study designs, the design must be taken into consideration [8]. Combining results across independent-groups and repeated measures designs requires that all effect sizes be transformed into a common metric, that effect sizes from each design estimate the same treatment effect, and that meta-analysis procedures use design-specific estimates of sampling variance to reflect the precision of the effect size estimates [8].

The effect size calculation should be documented for each study. This documentation should include the raw data used, the formula applied, and the resulting effect size and variance. This record allows other researchers to verify your calculations and allows you to check for errors.

For studies that report results in formats that cannot be directly converted, the protocol should specify the approach. Options include contacting authors for raw data, using published formulas to convert between metrics, or excluding the study from the quantitative synthesis.

Design Heterogeneity and Evidence Mapping

Design heterogeneity refers to the methodological, epidemiological, clinical, and biological dissimilarity across studies [7]. Meta-analysis provides a framework for the appreciation and assessment of between-study heterogeneity [7]. However, assessment of design heterogeneity conducted prior to meta-analysis is infrequently reported, and it is often presented post hoc to explain statistical heterogeneity [11].

Design heterogeneity determines the mix of included studies and how they are analyzed in a meta-analysis, which in turn can importantly influence the results [11]. A technique called evidence mapping can be used to organize studies and evaluate design heterogeneity prior to meta-analysis [11]. Evidence mapping involves systematically evaluating variation in definitions of key variables, design features, population characteristics, and modeling strategies across the included studies [11].

In the sugar-sweetened beverages and type 2 diabetes example, evidence mapping revealed that across 11 studies, 7 measured diet only once with 7 to 16 years of disease follow-up, 5 included primarily low consumers of sugar-sweetened beverages, and 3 defined the study variable as consumption of either sugar or artificially sweetened beverages [11]. The exercise also identified diversity in analysis strategies, such as adjustment for 11 to 17 co-variables and a large degree of fluctuation in risk estimates depending on which variables were selected for multivariable models [11].

Evidence mapping should be conducted before the statistical synthesis and reported in the results. The evidence map can take the form of a table or figure that shows the design features of each study. This map helps you decide whether studies are sufficiently similar to combine and helps readers understand the sources of heterogeneity.

Statistical Models for Synthesis

The choice of statistical model for the meta-analysis should be specified in the protocol and justified based on the expected heterogeneity. The two main models are fixed-effect and random-effects.

A fixed-effect model assumes that all studies estimate the same underlying effect and that differences between studies are due to sampling error alone. A random-effects model assumes that studies estimate different effects that are distributed around a central value, and it incorporates between-study variance into the weights.

Random-effects models are commonly used when heterogeneity is expected. A meta-analysis on daily steps and all-cause mortality used inverse-variance weighted random effects models [20]. A network meta-analysis on high-intensity interval training used a frequentist random effects model [14]. The choice of model should be based on the design heterogeneity assessment, not on the results of the analysis.

For dose-response meta-analyses, the analysis models the relationship between the exposure and the outcome across the range of exposure values. A review on daily steps and health outcomes synthesized hazard ratios from individual studies using random-effects dose-response meta-analysis where possible [17]. The results showed inverse non-linear dose-response associations for all-cause mortality, cardiovascular disease incidence, dementia, and falls, with inflection points at around 5000 to 7000 steps per day [17].

The statistical analysis should include methods for assessing heterogeneity, such as the I-squared statistic and the Q statistic. A network meta-analysis on high-intensity interval training reported substantial heterogeneity and significant inconsistency with an I-squared of 69.2 percent [14]. These statistics should be reported with confidence intervals, and the protocol should specify how heterogeneity will be explored if it is found.

Network Meta-Analysis Considerations

Network meta-analysis extends the standard meta-analysis framework to compare multiple interventions using direct and indirect evidence. This approach allows comparisons between interventions that have not been directly compared in head-to-head trials.

A network meta-analysis on high-intensity interval training protocol designs compared different protocol structures with respect to accumulated time at or above 90 percent of maximal oxygen uptake using direct and indirect evidence [14]. The researchers classified protocols using predefined operational rules based on interval duration, work-to-rest structure, intensity pattern, and within-session progression [14]. The analysis used standardized mean differences within a random effects model, with even long intervals as the reference condition, and added local inconsistency and contribution diagnostics [14].

Network meta-analysis requires additional assumptions beyond those of standard meta-analysis. The transitivity assumption requires that the studies are sufficiently similar in terms of the distribution of effect modifiers across the comparisons. The consistency assumption requires that direct and indirect evidence agree. These assumptions should be assessed and reported.

The results of a network meta-analysis should be presented with the network graph, the league table of comparisons, and the ranking of interventions. The ranking should be presented with uncertainty, such as the surface under the cumulative ranking curve or P-scores. In the high-intensity interval training example, P-score ranking favored decreased-duration protocols at 89.6 percent, followed by varied-intensity at 76.7 percent and decreased-intensity at 68.4 percent [14].

Special Designs: Single-Case and Multilevel Modeling

Meta-analysis is not limited to group-design studies. Single-case design research can also be synthesized, and multilevel modeling provides a framework for this purpose.

A study on meta-analyses of single-case design research described the benefits and challenges of using multilevel modeling [9]. The researchers illustrated procedures for conducting meta-analyses using four-level multilevel modeling through open-source R code [9]. The demonstration used data from multiple-baseline or multiple-probe across-participant single-case design studies on word problem instruction for students with learning disabilities published between 1975 and 2023 [9].

The researchers explored changes in levels and trends between adjacent phases, such as baseline versus intervention and intervention versus maintenance [9]. They concluded that word problem solving of students with learning disabilities varies based on the complexity of the word problem measures involving single-word problems, mixed-word problems, and generalization questions [9]. These moderating effects differed across adjacent phases [9].

Multilevel modeling allows researchers to explore the roles of time-varying predictors as well as case or study-level moderators [9]. This approach is particularly useful when the research question involves changes over time within cases. The choice of this method should be specified in the protocol, and the analysis should account for the nested structure of the data.

Bayesian Approaches and Historical Data

Bayesian methods offer an alternative framework for meta-analysis, particularly when historical information is available. Historical information is always relevant for clinical trial design, and if incorporated in the analysis of a new trial, historical data allow the reduction of the number of subjects [10]. This decreases costs and trial duration, facilitates recruitment, and may be more ethical [10].

A Bayesian meta-analytic-predictive prior can be derived from historical data and then combined with new data [10]. This prospective approach is equivalent to a meta-analytic-combined analysis of historical and new data if parameters are exchangeable across trials [10]. The researchers proposed two- or three-component mixtures of standard priors, which allow for good approximations and straightforward posterior calculations [10].

Under prior-data conflict, a too optimistic use of historical data may be inappropriate [10]. The mixture priors are often heavy-tailed and therefore robust, and further robustness can be achieved by adding an extra weakly-informative mixture component [10]. Use of historical prior information is particularly attractive for adaptive trials, as the randomization ratio can then be changed in case of prior-data conflict [10].

The decision to use Bayesian methods should be made at the protocol stage and justified based on the availability and quality of historical data. The choice of prior distributions should be specified, and sensitivity analyses should examine the impact of different priors.

Reporting Standards and Publication

The reporting of a meta-analysis should follow a recognized reporting guideline. The PRISMA checklist and flow diagram are commonly used [16]. The EQUATOR Network maintains a collection of reporting guidelines for health research [2].

The PRISMA flow diagram shows the number of records identified through database searching, the number of additional records identified through other sources, the number of records after duplicates removed, the number screened, the number excluded, the number of full-text articles assessed for eligibility, the number excluded with reasons, and the number of studies included in qualitative and quantitative synthesis [16].

The PRISMA checklist includes items on the title, abstract, introduction, methods, results, discussion, and funding. The methods section should describe the protocol and registration, the eligibility criteria, the information sources, the search strategy, the study selection process, the data collection process, the risk of bias assessment, and the synthesis methods.

The results section should present the study selection flow diagram, the study characteristics, the risk of bias within studies, the results of individual studies, the synthesis of results, the risk of bias across studies, and the additional analyses. The discussion should summarize the main findings, limitations, and conclusions.

Common Failure Patterns

Several recurring problems undermine the validity of meta-analyses. Recognizing these patterns can help you avoid them in your own work.

The first failure pattern is an unclear research question that does not map to the search strategy or inclusion criteria. If the question is vague, the search will be incomplete, and the inclusion criteria will be applied inconsistently.

The second failure pattern is an incomplete search. Searching only one database, restricting by language without justification, or failing to search for unpublished studies can introduce selection bias. The search should be comprehensive and documented.

The third failure pattern is pooling studies that are too heterogeneous. When studies differ in design, population, exposure definition, or outcome measurement, the pooled estimate may not represent any meaningful population. Evidence mapping prior to synthesis can help identify these differences [11].

The fourth failure pattern is ignoring design heterogeneity and only assessing statistical heterogeneity after the results are known [11]. Design heterogeneity should be assessed before the statistical synthesis, because it determines the mix of included studies and how they are analyzed [11].

The fifth failure pattern is using a fixed-effect model when substantial heterogeneity is present. The choice of model should be based on the design heterogeneity assessment and specified in the protocol.

The sixth failure pattern is inadequate reporting. Omitting search dates, failing to report excluded studies with reasons, or not providing the full search strings makes it impossible for readers to assess the validity of the review.

Limitations and Interpretation

Meta-analysis has inherent limitations that should be acknowledged in the protocol and the final report. Being a retrospective research design in most cases, meta-analysis is subject to a variety of selection biases that may undermine its validity [7]. A major challenge is to differentiate genuine between-study heterogeneity from systematic errors and biases [7].

The results of a meta-analysis depend on the quality of the included studies. If the primary studies have methodological flaws, the pooled estimate will reflect those flaws. The quality appraisal should be reported, and the limitations of the evidence base should be discussed.

The interpretation of the pooled estimate should consider the clinical or practical significance of the effect, beyond the statistical significance. The confidence interval provides a range of plausible values, and the protocol should specify how the results will be interpreted in the context of the research question.

The applicability of the evidence should be considered. The protocol should specify the population to which the results are intended to apply, and the discussion should address whether the included studies represent that population.

Professional Escalation Criteria

Some situations require escalation to a statistician or methodological expert. If you encounter any of the following situations, seek expert advice before proceeding.

If the included studies report data in formats that cannot be converted to a common metric, consult a statistician about the available conversion methods. If the heterogeneity is substantial and cannot be explained by the prespecified moderators, consult a statistician about alternative synthesis methods. If the network meta-analysis shows significant inconsistency, consult a statistician about the appropriate diagnostic and adjustment methods.

If the search identifies a very large number of studies, consult a librarian or information specialist about the search strategy and screening process. If the quality appraisal reveals serious flaws in the included studies, consult a methodological expert about whether the synthesis should proceed.

If the research question involves a topic with known methodological controversies, such as the pooling of data from trials using different therapeutic agents, consult a content expert about the appropriateness of the synthesis [13]. The pooling of data from trials using different interventions to assess the overall success of a therapeutic approach has been criticized, and the protocol should justify any such pooling [13].

Records and Measurements

The records you keep during a meta-analysis serve as the evidence that the review was conducted systematically. The following records should be maintained and made available to readers.

The protocol should be dated and versioned, with amendments documented. The search records should include the databases searched, the search strings, the date each search was run, and the number of records retrieved. The screening records should include the number of records screened, the number excluded, and the reasons for exclusion.

The data extraction records should include the extraction form, the data extracted from each study, and the agreement between reviewers. The quality appraisal records should include the appraisal tool, the ratings for each study, and the agreement between reviewers.

The analysis records should include the effect size calculations, the statistical model, the heterogeneity statistics, and the sensitivity analyses. The reporting records should include the PRISMA checklist and flow diagram [16].

Practical Implementation Steps

The following steps provide a practical sequence for designing and conducting a meta-analysis.

First, define the research question using the PICO framework. Write the question in a form that can be answered by the studies you expect to find.

Second, develop the protocol. Specify the search strategy, inclusion criteria, data extraction process, quality appraisal method, and statistical analysis plan. Register the protocol if a registry is available.

Third, conduct the search. Run the search strings in each database and record the date and results. Export the records to a reference management tool and remove duplicates.

Fourth, screen the records. Apply the inclusion criteria to the titles and abstracts, then to the full texts. Record the reasons for exclusion.

Fifth, extract the data. Use a pilot-tested extraction form and have two reviewers extract data independently.

Sixth, appraise the quality of the included studies. Use a validated tool appropriate for the study designs.

Seventh, conduct the evidence mapping. Assess design heterogeneity before the statistical synthesis and report the evidence map.

Eighth, calculate the effect sizes and conduct the statistical synthesis. Use the model specified in the protocol and report the heterogeneity statistics.

Ninth, conduct sensitivity analyses. Examine the impact of excluding low-quality studies, using different effect size metrics, or using different statistical models.

Tenth, report the results. Follow the PRISMA checklist and flow diagram [16]. Report the search dates, the excluded studies, and the limitations.

Frequently Asked Questions

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

A systematic review applies scientific methods to reduce bias in the review of literature, and its key components include a well-defined research question, a comprehensive literature search, systematic assembly of studies, critical appraisal of methodological quality, data extraction and analysis, and consideration of the applicability of the evidence [6]. A meta-analysis is a statistical tool that provides pooled estimates of effect from the data extracted from individual studies in the systematic review [6]. A systematic review can be conducted without a meta-analysis if the studies are too heterogeneous to combine.

How do I choose between a fixed-effect and a random-effects model?

The choice of model should be based on the design heterogeneity assessment and specified in the protocol. A fixed-effect model assumes that all studies estimate the same underlying effect, while a random-effects model assumes that studies estimate different effects distributed around a central value. Random-effects models are commonly used when heterogeneity is expected, and they incorporate between-study variance into the weights.

What is design heterogeneity and why does it matter?

Design heterogeneity refers to the methodological, epidemiological, clinical, and biological dissimilarity across studies [7]. It determines the mix of included studies and how they are analyzed in a meta-analysis, which in turn can importantly influence the results [11]. Design heterogeneity should be assessed prior to the statistical synthesis using techniques such as evidence mapping [11].

How do I handle studies that report data in different formats?

All effect sizes must be transformed into a common metric before they can be combined [8]. The protocol should specify the approach for studies that report data in formats that cannot be directly converted. Options include contacting authors for raw data, using published formulas to convert between metrics, or excluding the study from the quantitative synthesis.

What is evidence mapping and when should I use it?

Evidence mapping is a technique used to organize studies and evaluate design heterogeneity prior to meta-analysis [11]. It involves systematically evaluating variation in definitions of key variables, design features, population characteristics, and modeling strategies across the included studies [11]. Evidence mapping should be conducted before the statistical synthesis and reported in the results.

Can I combine results from different study designs in a meta-analysis?

Combining results across designs requires that all effect sizes be transformed into a common metric, that effect sizes from each design estimate the same treatment effect, and that meta-analysis procedures use design-specific estimates of sampling variance to reflect the precision of the effect size estimates [8]. The conditions under which such an analysis is appropriate should be assessed and reported.

What is a network meta-analysis and when should I use it?

A network meta-analysis compares multiple interventions using direct and indirect evidence. It is useful when you need to compare interventions that have not been directly compared in head-to-head trials. Network meta-analysis requires additional assumptions beyond those of standard meta-analysis, including transitivity and consistency, and these assumptions should be assessed and reported.

What should I do if the included studies have serious methodological flaws?

The quality appraisal results should be reported for each included study, and the protocol should specify how quality will be incorporated into the synthesis. Options include excluding low-quality studies in sensitivity analyses, weighting studies by quality, or examining quality as a moderator. If the flaws are serious, consult a methodological expert about whether the synthesis should proceed.

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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.
• Systematic Reviews and Meta-Analysis: A Guide for Beginners.. Indian pediatrics, 2022.
• Meta-analysis methods.. Advances in genetics, 2008.
• Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs.. Psychological methods, 2002.
• Meta-analysis of single-case design research: Application of multilevel modeling.. School psychology (Washington, D.C.), 2024.
• Robust meta-analytic-predictive priors in clinical trials with historical control information.. Biometrics, 2014.
• Evidence-based mapping of design heterogeneity prior to meta-analysis: a systematic review and evidence synthesis.. Systematic reviews, 2014.
• Randomized, controlled trials, observational studies, and the hierarchy of research designs.. The New England journal of medicine, 2000.
• Homeopathy.. The Medical clinics of North America, 2002.
• Comparison of high-intensity interval training protocol designs on accumulated time ≥ 90% V̇O₂max: a network meta-analysis.. 2026.
• Applications of Large Language Models in Ovarian Cancer Management: Protocol for a Systematic Review and Meta-Analysis.. 2026.
• Meta-Analysis Steps and Reporting. Turkish Journal of Family Medicine & Primary Care, 2019.
• Daily steps and health outcomes in adults: a systematic review and dose-response meta-analysis.. Lancet Public Health, 2025.
• The global burden of overweight-obesity and its association with economic status, benefiting from STEPs survey of WHO member states: A meta-analysis. Preventive medicine reports, 2024.
• Daily steps and all-cause mortality: An umbrella review and meta-analysis.. Preventive Medicine, 2024.
• Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts. Lancet Public Health, 2022.
• Meta-Analysis of Single-Case Design Research: Introduction to the Special Issue. Journal of Behavioral Education, 2012.

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