Group-sequential & futility design
Set up, run, and review this analysis in Lifescio.
Supported analysis
The app calibrates O’Brien–Fleming-shaped or Pocock-shaped one-sided efficacy boundaries under a canonical joint-normal model. Simulations under null and alternative effects report rejection, stopping and expected sample size. Futility is nonbinding.
Supported scope: The model assumes equally allocated independent normal outcomes with known variance. It is one-sided, with a fixed analysis schedule. Survival, delayed outcomes, binding futility and unplanned looks need separate design work.
Prepare data and configure the analysis
Choose the O'Brien–Fleming-shaped or Pocock-shaped efficacy boundary, Number of equally spaced looks, and Maximum total sample size across both arms. Sample sizes are rounded to even totals for equal allocation. Standardized treatment effect is the mean difference divided by the known individual standard deviation.
Set One-sided alpha, the interim Nonbinding Z futility boundary, and simulation replications. The futility boundary must be below every efficacy boundary. This is a known-variance normal design simulator, not a general spending-function editor or a data-monitoring service.
Input reference (7 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 |
|---|---|---|---|
| Efficacy boundary | Selection | — | Choices: obrien-fleming, pocock. |
| Number of equally spaced looks | Whole number | 1–6 | — |
| Maximum total sample size | Whole number | 20–10000 | — |
| Standardized treatment effect | Number | -2–2 | — |
| One-sided alpha | Number | 0.001–0.1 | — |
| Nonbinding interim Z futility boundary | Number | -3–2 | — |
| Simulation replications | Whole number | 100–20000 | — |
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
Analysis boundaries lists information fractions, actual rounded N, efficacy Z, and interim futility Z. Operating characteristics reports null and alternative success probabilities, Monte Carlo uncertainty, and expected N.
Stopping probabilities gives the fraction of all simulated trials stopping at each look, not the fraction conditional on reaching that look. Diagnostics include numerically integrated Type I error without futility. Compare it with the simulated null result while allowing for the applied futility rule and simulation error.
Troubleshooting and common mistakes
If the futility boundary is rejected, check its relation to the efficacy boundaries rather than changing alpha to accommodate it. Rounded look sizes should be inspected when total enrollment is small.
If estimated null error fluctuates across seeds, inspect its Monte Carlo interval and increase replications as needed. This workflow does not account for delayed outcomes, nonnormal endpoints, or deviations from planned information timing. Those require design-specific evaluation.
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.