Meta-analysis and systematic review methods provide a structured and scientifically rigorous approach to synthesizing evidence from multiple studies. While a systematic review identifies, evaluates, and summarizes the available literature, a meta-analysis statistically combines comparable study findings to estimate an overall effect.
At Data Miner Statistics, meta-analysis and systematic review processes are carefully planned according to the research question, study design, eligibility criteria, outcome variables, and methodological characteristics of the available evidence. MAXQDA can be used to organize publications, manage study-screening decisions, code qualitative findings, develop themes, and conduct structured evidence synthesis.
Systematic Review Services
A systematic review follows a transparent and reproducible process for identifying, selecting, evaluating, and synthesizing relevant studies. The process begins with a clearly defined research question and continues through literature searching, study screening, data extraction, quality assessment, and evidence synthesis.
Systematic review services may include:
- Development of the research question
- Preparation of review objectives
- Definition of inclusion and exclusion criteria
- Development of a review protocol
- Selection of academic databases
- Development of search terms and Boolean operators
- Title and abstract screening
- Full-text eligibility assessment
- Duplicate-publication management
- Study-characteristics extraction
- Methodological quality assessment
- Risk-of-bias evaluation
- Qualitative evidence synthesis
- Preparation of PRISMA flow diagrams
- Reporting according to PRISMA guidelines
Each stage should be documented clearly so that readers can understand how studies were identified, selected, evaluated, and included in the final review.
MAXQDA for Systematic Reviews
MAXQDA provides useful tools for organizing, coding, categorizing, and synthesizing research publications. It is particularly valuable for systematic reviews that include qualitative studies, mixed-method studies, textual data, or complex thematic findings.
MAXQDA-supported systematic review processes may include:
- Importing academic articles and research reports
- Organizing publications into document groups
- Coding study objectives, methods, samples, and findings
- Developing hierarchical code systems
- Recording inclusion and exclusion decisions
- Creating document variables
- Comparing studies according to methodological characteristics
- Identifying recurring concepts and themes
- Conducting qualitative content analysis
- Retrieving coded segments
- Creating code matrices and comparison tables
- Developing thematic maps and conceptual models
- Supporting transparent evidence synthesis
MAXQDA can help researchers manage large numbers of publications systematically and maintain a clear record of the analytical process.
Qualitative Evidence Synthesis
Not every systematic review is suitable for statistical meta-analysis. When studies differ substantially in design, participants, interventions, outcomes, or measurement methods, a qualitative or narrative synthesis may be more appropriate.
Qualitative evidence synthesis may include:
- Thematic synthesis
- Narrative synthesis
- Qualitative content analysis
- Framework synthesis
- Meta-aggregation
- Mixed-method evidence integration
- Comparison of themes across studies
- Identification of research gaps
- Development of conceptual categories
- Interpretation of similarities and differences among findings
MAXQDA supports these processes by allowing researchers to code findings systematically and compare evidence across publications.
Meta-Analysis Services
Meta-analysis statistically combines the results of independent studies addressing a similar research question. It provides an overall effect estimate and allows researchers to evaluate differences among studies.
Meta-analysis services may include:
- Development of the meta-analysis protocol
- Identification of suitable effect-size measures
- Extraction of statistical data
- Calculation of effect sizes
- Fixed-effect model analysis
- Random-effects model analysis
- Assessment of statistical heterogeneity
- Subgroup analysis
- Moderator analysis
- Meta-regression
- Sensitivity analysis
- Influence analysis
- Publication-bias assessment
- Forest-plot preparation
- Funnel-plot preparation
- Interpretation of pooled results
The choice of analytical model depends on the research question, study characteristics, level of heterogeneity, and assumptions concerning the underlying effects.
Effect-Size Calculation
Effect size represents the magnitude of the relationship, difference, or intervention effect reported across studies. The appropriate effect-size measure depends on the study design and the type of outcome.
Common effect-size measures include:
- Cohen’s d
- Hedges’ g
- Standardized mean difference
- Mean difference
- Correlation coefficient
- Fisher’s z-transformed correlation
- Odds ratio
- Risk ratio
- Risk difference
- Hazard ratio
- Prevalence estimates
Effect sizes should be calculated consistently and accompanied by confidence intervals. When studies use different measurement scales, standardized effect-size measures may be required.
Heterogeneity Assessment
Heterogeneity refers to differences in effect estimates across studies. These differences may arise from variations in study populations, research settings, interventions, measurement instruments, sample characteristics, or methodological quality.
Heterogeneity assessment may include:
- Cochran’s Q statistic
- I² statistic
- Tau-squared estimation
- Prediction intervals
- Subgroup analysis
- Moderator analysis
- Meta-regression
A statistically significant overall effect does not eliminate the need to evaluate heterogeneity. Researchers should determine whether differences between studies influence the interpretation and generalizability of the pooled result.
Publication-Bias Assessment
Publication bias may occur when studies with statistically significant or favourable results are more likely to be published than studies with nonsignificant findings.
Publication-bias assessment may include:
- Funnel-plot inspection
- Egger’s regression test
- Begg’s rank-correlation test
- Trim-and-fill analysis
- Fail-safe N calculation
- Small-study-effect assessment
- Sensitivity analysis
Publication-bias results should be interpreted cautiously because funnel-plot asymmetry can also result from heterogeneity, methodological differences, sampling variation, or study quality.
Subgroup Analysis and Meta-Regression
Subgroup analysis and meta-regression are used to investigate why study findings differ. These methods help determine whether effect sizes vary according to specific study characteristics.
Potential moderators may include:
- Participant age
- Gender distribution
- Geographic region
- Study design
- Measurement instrument
- Intervention duration
- Sample size
- Publication year
- Methodological quality
- Clinical or demographic characteristics
Moderator analyses should be theoretically justified and planned carefully. Conducting many exploratory comparisons without sufficient evidence may produce unstable or misleading results.
Risk-of-Bias and Quality Assessment
The reliability of a systematic review or meta-analysis depends on the methodological quality of the included studies. Appropriate assessment tools should be selected according to the research design.
Quality and risk-of-bias assessment may include:
- Randomized controlled trial assessment
- Observational study assessment
- Cohort and case-control study evaluation
- Cross-sectional study appraisal
- Qualitative study appraisal
- Diagnostic accuracy assessment
- Mixed-method study evaluation
- Evaluation of missing data and selective reporting
- Assessment of confounding and measurement bias
Quality assessments should not be treated as a simple checklist. Their implications for the review findings should also be discussed.
PRISMA-Compliant Reporting
Systematic reviews and meta-analyses should be reported transparently. PRISMA-based reporting helps readers evaluate how the review was planned, conducted, and documented.
PRISMA reporting may include:
- Review objectives and research questions
- Information sources and databases
- Complete search strategy
- Eligibility criteria
- Study-selection process
- Data-extraction procedures
- Risk-of-bias methods
- Synthesis methods
- PRISMA flow diagram
- Characteristics of included studies
- Results of individual studies
- Overall synthesis findings
- Limitations and certainty of evidence
Clear reporting improves reproducibility and strengthens the scientific credibility of the review.
MAXQDA and Statistical Meta-Analysis
MAXQDA is particularly useful for literature organization, article coding, qualitative analysis, thematic synthesis, and systematic review management. Statistical meta-analysis calculations are generally completed using specialist statistical software after the relevant quantitative data have been extracted.
An integrated workflow may include:
- Organizing and screening publications
- Coding articles and study characteristics in MAXQDA
- Extracting quantitative findings
- Calculating effect sizes
- Conducting statistical meta-analysis
- Preparing forest and funnel plots
- Integrating quantitative and qualitative findings
- Reporting the results according to PRISMA standards
This combined approach supports both structured literature management and scientifically rigorous statistical synthesis.
Accurate Evidence Synthesis and Publication-Ready Reporting
A systematic review or meta-analysis requires more than collecting published studies. The research question, search strategy, eligibility criteria, data-extraction procedures, quality assessment, effect-size calculations, heterogeneity, and reporting standards must be addressed systematically.
Professional support may include:
- Systematic review protocol development
- Search-strategy preparation
- Study-screening management
- MAXQDA coding and qualitative synthesis
- Data-extraction table preparation
- Risk-of-bias assessment
- Effect-size calculation
- Statistical meta-analysis
- Subgroup and moderator analysis
- Publication-bias assessment
- PRISMA flow-diagram preparation
- Publication-ready tables and figures
- Scientific interpretation of findings
- Methodology and results-section writing
- Evaluation of reviewer comments
At Data Miner Statistics, systematic reviews and meta-analyses are conducted with methodological transparency, analytical accuracy, and careful reporting.
Contact Data Miner Statistics for professional meta-analysis, systematic review, MAXQDA coding, qualitative evidence synthesis, PRISMA reporting, and publication-ready research support.
Biostatistics applies statistical methods to medical, clinical, biological, dental, nursing, pharmaceutical, epidemiological, and public health research. It supports researchers in designing scientifically valid studies, selecting appropriate statistical methods, analyzing health-related data, and interpreting results accurately.
At Data Miner Statistics, biostatistical analyses are planned according to the research question, study design, outcome variables, sample characteristics, measurement level, and distributional properties of the data. Each analysis is conducted systematically, from data preparation and assumption testing to statistical interpretation and publication-ready reporting.
The purpose is not simply to obtain a statistically significant result, but to select the correct method and present the findings transparently.
Variables should not be entered into a regression model solely according to statistical significance. Clinical relevance, theoretical importance, sample size, and potential confounding should also be considered.
Survival results are commonly presented using Kaplan–Meier curves, hazard ratios, confidence intervals, event counts, and survival probabilities.