Survival trial sample size
Set up, run, and review this analysis in Lifescio.
Supported analysis
A Schoenfeld approximation links the target hazard ratio to required events. Exponential survival, uniform accrual, additional follow-up and exponential dropout translate that target into participant counts.
Supported scope: Hazards must be proportional under the planning model. Median survival, accrual and follow-up must use the same time unit. Delayed treatment effects, changing hazards or nonuniform enrollment are not represented.
Prepare data and configure the analysis
Use months consistently for Control median survival, Accrual duration, and Additional follow-up. Monthly dropout hazard is an instantaneous rate, not the fraction lost in one month: a monthly dropout probability p corresponds to a constant hazard of −log(1−p).
Enter the Treatment/control hazard ratio, allocation ratio, power, and significance level. This workflow uses a two-sided event-count approximation, exponential survival, and uniform entry during accrual. Additional follow-up begins after recruitment ends. A hazard ratio of one is invalid because it specifies no treatment difference.
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 |
|---|---|---|
| Treatment/control hazard ratio | Number | 0.01–5 |
| Control median survival (months) | Number | 0.1–1000 |
| Accrual duration (months) | Number | 0.1–1000 |
| Additional follow-up (months) | Number | 0–1000 |
| Monthly dropout hazard | Number | 0–1 |
| Treatment/control allocation ratio | Number | 0.1–10 |
| Target power | Number | 0.5–0.999 |
| 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
In Event-driven planning, Required events is the rounded information target. Control N and Treatment N are projected enrollment, with dropout already incorporated in the event probabilities. Expected events combines those group sizes and probabilities.
Control event probability and Treatment event probability refer to observed events within the proposed study, not lifetime risks. Approximate power uses the projected events. Avoid adding a second blanket dropout inflation to these enrollment numbers without understanding that dropout is already in the model.
Troubleshooting and common mistakes
If enrollment becomes very large, inspect short follow-up, low baseline event rates, high dropout, and a hazard ratio close to one. Confirm that a yearly rate was not entered as a monthly hazard.
A plausible event total does not validate proportional hazards. If delayed or crossing treatment effects are expected, or recruitment is highly uneven, this simple calculation is not a sufficient design justification. Retain alternative scenarios alongside the primary planning run.
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.