Adaptive sample-size re-estimation
Set up, run, and review this analysis in Lifescio.
Supported analysis
Stage-one evidence estimates an effect and conditional power. The app applies a futility rule or chooses a second-stage size within the planned size and cap. A fixed-weight inverse-normal combination test combines independent stage statistics.
Supported scope: This is a known-variance, two-arm normal-outcome model with independent stages. Adaptation rules and weights must be prespecified. It does not support unrestricted adaptations, arbitrary endpoints or a naive pooled final test after resizing.
Prepare data and configure the analysis
Enter Stage 1 total participants, Planned stage 2 participants, and Maximum stage 2 participants as totals across both arms. The cap refers to stage 2 alone, so maximum trial enrollment is stage 1 plus the cap. Equal allocation produces even stage sizes.
Enter standardized effect, Conditional power target, Conditional power futility threshold, one-sided alpha, and replications. Stage 2 cannot fall below its planned size unless the trial stops for futility. Its adaptation uses the stage-1 observed effect; the combination-test weights stay fixed from the original planned sizes.
Input reference (8 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 |
|---|---|---|
| Stage 1 total participants | Whole number | 20–5000 |
| Planned stage 2 participants | Whole number | 20–5000 |
| Maximum stage 2 participants | Whole number | 20–10000 |
| Standardized treatment effect | Number | -2–2 |
| Conditional power target | Number | 0.5–0.99 |
| Conditional power futility threshold | Number | 0.01–0.49 |
| One-sided alpha | Number | 0.001–0.1 |
| 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
Adaptive operating characteristics reports null and alternative success, simulation uncertainty, expected total N, and probabilities of futility, an increase, and hitting the cap. These are unconditional frequencies across simulated trials.
Combination test displays the rounded stage sizes, fixed weights, and critical combined Z. Review that table when comparing scenarios. A high conditional-power target can coexist with lower overall power if futility stops or the cap prevents the requested increase.
Troubleshooting and common mistakes
A cap below the planned second stage is invalid. Check whether you entered a whole-trial cap instead of a stage-2 cap. Also inspect even-number rounding before describing enrollment limits.
If cap probability is high, the policy often cannot recruit what the rule requests. Increasing replications only estimates that fact more precisely. Reevaluate any revised cap, stage sizes, or thresholds under both null and alternative scenarios; do not replace the supported combination test with a naive pooled final test.
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.