Move from method selection and effect-size harmonization to statistical synthesis, forest plots, sensitivity analysis, and structured risk-of-bias assessment in one connected browser workflow.
Cloud-Based No Install Free Guest Access APA Output For Researchers
What does the DataStatPro meta-analysis suite do?
DataStatPro supports four connected evidence-synthesis tasks: selecting a defensible analysis method, converting reported study statistics into compatible effect sizes, pooling study estimates with fixed-effect or random-effects methods, and recording risk-of-bias judgments. The suite produces statistical tables, forest and funnel plots, diagnostics, sensitivity analyses, and exportable reports while stating when a specialized external model is still required.
Comprehensive Effect Sizes
Fixed & Random Effects Models
Interactive Forest Plots
Publication Bias Detection
Four tools for a complete meta-analysis workflow
Use the tools independently or follow them in sequence. The Method Advisor helps define the estimand and analysis specification; MetaConvert harmonizes study results; Meta Analysis performs the synthesis; and Risk of Bias Assessment documents internal-validity concerns that must inform interpretation.
Meta-Analysis Method Advisor
Answer a guided series of questions about outcome type, effect measure, study count, clinical diversity, sparse data, zero cells, and inference goals.
Distinguishes fixed-effect, random-effects, and specialized hierarchical questions.
Convert differently reported study results into a common effect-size representation before pooling.
Works with 2×2 tables, OR and RR inputs, means and standard deviations, standardized mean differences, t statistics, and correlations.
Produces analysis-ready combinations such as log odds ratio with SE, Hedges g with SE, Fisher z with SE, risk ratio with CI, and risk difference with SE.
Shows formulas, assumptions, warnings, and worked conversions for auditability.
Define the question and estimand. Use the Method Advisor before examining heterogeneity statistics. Decide which outcome, effect measure, population, comparison, and time point the synthesis will address.
Prepare and harmonize study results. Use MetaConvert when papers report different but convertible statistics. Confirm that every effect and standard error is on the scale expected by the analysis.
Run the statistical synthesis. Map study labels and estimates, select the prespecified model and inference method, then inspect the pooled estimate, confidence interval, heterogeneity, and prediction interval where appropriate.
Investigate robustness. Review forest and funnel plots, subgroup or moderator analyses, leave-one-out results, influence diagnostics, and prespecified alternative estimators or zero-cell strategies.
Integrate risk of bias. Complete the appropriate framework and interpret the pooled result alongside study limitations. A precise pooled estimate does not remove bias in the underlying evidence.
Export and report transparently. Record the effect scale, weighting method, model, tau-squared estimator, confidence-interval method, excluded studies, corrections, sensitivity analyses, and limitations.
Meta-analysis methods and outputs
Research outcome
Typical effect measure
DataStatPro pathway
Reference value
Continuous outcome on the same scale
Mean difference (MD)
Raw group statistics or pre-calculated MD and SE
0
Continuous outcome on different scales
Standardized mean difference (SMD), including Hedges g
MetaConvert or pre-calculated effect and SE
0
Binary outcome
Odds ratio, risk ratio, or risk difference
2×2 counts or pre-calculated effects; zero-cell strategy must be stated
1 for ratios; 0 for RD
Time-to-event outcome
Hazard ratio (HR)
Log HR and SE, with reported-scale back-transformation
1
Incidence comparison
Incidence rate ratio (IRR)
Log IRR and SE
1
Association
Correlation
Fisher z pooling with back-transformation to r
0
Single-arm prevalence or event frequency
Proportion
Events and total or a compatible pre-calculated effect
Context dependent
Important limitation: not every evidence-synthesis model has a direct DataStatPro equivalent. The module supports a binomial-normal GLMM for eligible single-group proportions. Multilevel dependent-effect models, network meta-analysis, and bivariate or HSROC diagnostic-accuracy models may require specialist software. The Method Advisor identifies these cases rather than presenting a simpler model as equivalent.
Comprehensive Effect Size Support
Analyze diverse research outcomes with support for Odds Ratios (OR), Risk Ratios (RR), Hazard Ratios (HR), Risk Differences (RD), and Proportions. DataStatPro handles the complexities, allowing you to focus on interpretation.
Fixed & Random Effects Models
Choose between fixed-effect and random-effects models based on your research question and the heterogeneity of your studies. DataStatPro provides clear guidance and robust calculations for both approaches.
Interactive Forest Plots
Visualize your meta-analysis results with stunning, interactive forest plots. Easily identify individual study effects, confidence intervals, and the overall pooled effect at a glance. Customize plots for publication.
Funnel Plots & Publication Bias
Assess publication bias and small-study effects with automatically generated funnel plots. DataStatPro helps you critically evaluate the integrity of your meta-analytic findings.
Subgroup Analysis & Meta-Regression
Explore sources of heterogeneity by conducting subgroup analyses or meta-regression. Investigate how study characteristics influence effect sizes with advanced analytical tools.
APA-Formatted Output
Generate publication-ready tables and figures in APA 7th edition style directly from your meta-analysis. Save hours of formatting time and ensure compliance with academic standards.
Flexible Data Import
Effortlessly import your study data from Excel (.xlsx) or CSV files with simple drag-and-drop functionality. DataStatPro intelligently recognizes your data structure, making setup quick and easy.
Cloud-Based & Cross-Platform
Access your meta-analysis projects from anywhere, on any device. DataStatPro runs entirely in your web browser, compatible with Mac, Windows, Linux, iOS, and Android, with no installation ever required.
Why Choose DataStatPro for Your Meta-Analysis?
Compared to traditional desktop software or complex statistical programming languages, DataStatPro offers a streamlined, accessible, and powerful solution for meta-analysis.
Feature
DataStatPro
Other Meta-Analysis Software (e.g., Comprehensive Meta-Analysis, R with packages)
Cost
Free Guest Access; plan options
Often expensive licenses or requires programming expertise (R)
Installation Required
No (Cloud-Based)
Yes (Desktop Software)
Operating System Compatibility
Any OS (Browser-based)
Limited (Windows/Mac specific, R is cross-platform but requires setup)
Effect Size Support
OR, RR, HR, RD, Proportion, etc.
Varies, often comprehensive in paid software or specific R packages
Forest Plots
Interactive & Customizable
Available, customization varies
Funnel Plots
Automatic Generation
Available, often requires manual steps or coding
APA-Formatted Output
Automatic
Manual formatting often required
Ease of Use
Extremely User-Friendly (Point-and-Click)
Can be complex, steep learning curve for R
Data Import
Drag-and-Drop (Excel, CSV)
Varies, often requires specific formats
How to Get Started with Meta-Analysis in DataStatPro
Prepare your data: Organize your study data (effect sizes, standard errors, sample sizes) in an Excel (.xlsx) or CSV file.
Launch DataStatPro: Go to datastatpro.com/app in your web browser. No account or installation needed.
Import your data: Drag and drop your Excel or CSV file into the DataStatPro interface.
Run Meta-Analysis: Select the Meta-Analysis module, map your variables, choose your effect size type and model, and run the analysis.
Export Results: Get instant APA-formatted tables and interactive plots ready for your research paper.
Frequently Asked Questions about Meta-Analysis
Meta-analysis software combines compatible effects from independent studies, quantifies uncertainty and heterogeneity, creates forest plots, and supports sensitivity analyses. It does not replace a systematic search, defensible eligibility criteria, data verification, or risk-of-bias assessment.
The suite includes the Meta-Analysis Method Advisor, MetaConvert, Meta Analysis, and Risk of Bias Assessment. They cover planning, effect-size harmonization, pooling, visualization, diagnostics, reporting, and structured appraisal.
Supported workflows include MD, SMD, OR, RR, HR, IRR, RD, correlation, and proportion. The required input may be a pre-calculated effect and standard error, raw continuous group statistics, or binary event counts.
Yes. Forest plots show study estimates, confidence intervals, weights, subgroups, pooled effects, and optional interpretation labels. Funnel plots support visual assessment of small-study effects, but asymmetry alone is not proof of publication bias.
MetaConvert turns commonly reported statistics into compatible effect-size and uncertainty formats. It supports workflows based on binary tables, ratio measures, continuous group summaries, standardized effects, t statistics, and correlations.
It asks about the outcome, estimand, study count, clinical diversity, sparse data, and generalization goal. It then explains the recommended effect measure, model, pooling method, tau-squared estimator, confidence interval, prediction interval, and sensitivity analyses.
The Risk of Bias Assessment tool includes Cochrane RoB 2, ROBINS-I, Newcastle-Ottawa Scale, Jadad, and AMSTAR 2 workflows with structured judgments, summary tables, traffic-light plots, heatmaps, and exports.
No. The module supports a binomial-normal GLMM for eligible single-group proportions, while dependent-effect multilevel models, network meta-analysis, and bivariate or HSROC diagnostic models may require specialist software. DataStatPro identifies these limitations without claiming equivalence.
Start an evidence-synthesis workflow
Begin with the Method Advisor if the analysis specification is undecided, use MetaConvert to harmonize reported results, or open Meta Analysis when effect estimates are ready.
Free Guest Access lets everyone teach and learn with built-in sample datasets. Free Educational Access with a valid .edu email includes sample datasets and your own datasets within Educational plan limits. Standard includes a 15-day free trial with no credit card required, then costs USD 39 per year. Pro provides full feature access for USD 89 per year; Edu Pro provides the same features for USD 49 per year with a valid .edu email, saving USD 40 per year (approximately 45%). View free access and trial details.
