Meta-regression
Set up, run, and review this analysis in Lifescio.
Supported analysis
Append numeric moderator columns to study effects and standard errors. The app fits a random-effects meta-regression with REML residual heterogeneity and small-sample t inference. Categorical moderators need explicit numeric coding.
Supported scope: Use independent studies and enough studies for the number of coefficients. Confounding across studies, moderator measurement error and exploratory selection can distort associations. This workflow does not support dependent effect sizes or automatic causal interpretation.
Prepare data and configure the analysis
Provide Effect, positive Standard error, then one or more numeric moderator columns. Effects and standard errors must already be on the intended analysis scale. All columns after standard error enter the regression, so remove unused columns rather than leaving example values.
Use reference-category indicators for categorical moderators and consider centering continuous moderators before import. The design must have full column rank and at least two residual degrees of freedom. The engine adds its own intercept; do not paste another constant column.
Input reference (2 fields)
Enter your data and prespecified choices. Ranges include both limits. For table fields, use cell editing, Paste data, or Import CSV / TSV. Review whether the first imported row contains headings and map the source columns before applying the import. Empty numeric cells are not treated as zero. Open file is for an exported Lifescio analysis JSON file, not a spreadsheet.
| Input | Format | Allowed range | Guidance |
|---|---|---|---|
| Study estimates | Table | — | Use log effects for ratios. Each row is an independent study; moderator is used only in meta-regression. Columns in order: Effect; Standard error; Moderator. |
| Significance level | Number | 0.001–0.2 | — |
Run and review
- Give the analysis a descriptive name and review every example value. Record units, data preparation, and planning assumptions in Reproducibility & notes. For simulations, record the seed and replication or sampling settings.
- Choose Run analysis. Resolve input errors before interpreting output. In the connected app, a run uses a saved input revision; local preview runs need the page to stay open.
- Review Diagnostics for method limitations, warnings, and available numerical checks. A completed run means computation finished; it does not establish that the assumptions fit your study.
- Use the Result table selector to inspect each output. Editing inputs does not recalculate existing results: an earlier-input notice means you need to run again to obtain matching results.
Understand the outputs
Meta-regression reports the intercept and Moderator 1, Moderator 2, etc., in input order. Coefficients, standard errors, and intervals remain on the entered effect scale; there is no automatic ratio transformation.
Residual heterogeneity contains tau squared, residual Q, and residual degrees of freedom. The observed-versus-fitted chart helps reveal poorly fitted studies but is not a causal validation. The implementation uses restricted maximum likelihood and modified Knapp–Hartung t intervals.
Troubleshooting and common mistakes
For rank deficiency, look for duplicate moderators, a constant column, or a full set of category indicators alongside the automatic intercept. For insufficient residual degrees of freedom, reduce the model only on defensible grounds or obtain more eligible studies.
Do not interpret a moderator p-value as patient-level effect modification. If results depend strongly on one study or a coding choice, report that sensitivity. Dependent effect estimates and nonlinear moderator models require a more suitable analysis.
After correcting inputs, run again and check that the results belong to the new input snapshot. Increasing simulation draws improves numerical precision only; it does not repair invalid data or an unsuitable model.
Save, compare, and export
In the connected app, Save analysis retains the inputs in your account. Run history lets you inspect earlier runs, each with the input revision it used. In local preview, Save analysis downloads an input file instead; download results before leaving the page.
Use Result + inputs for a JSON archive containing the completed result and its inputs, Download CSV for the selected result table, PDF report for a report, and a chart's SVG button for its figure. Table displays round to three decimals; CSV and JSON exports retain calculation precision. Use those values when assessing a result near a decision boundary.
To reopen an archive, select the matching analysis method and choose Open file. Imported inputs start a new draft; a result archive can also display its retained result. It does not attach itself to an existing account analysis or recompute the result. Save or run the imported draft when you want a new account record.