Patient microsimulation
Set up, run, and review this analysis in Lifescio.
Supported analysis
Each simulated patient starts from the initial distribution and draws a next state every cycle. The two strategies use independent patient streams. Costs and QALYs are averaged and compared with the deterministic cohort solution.
Supported scope: This workflow uses the same time-homogeneous matrices and state rewards as the cohort method. Patient history, individual risk factors and time-varying treatment effects are not modeled. A larger patient count does not address uncertain model parameters.
Prepare data and configure the analysis
Complete the state list, both transition matrices, annual rewards, initial proportions, cycles, and discounting as for a cohort model. Patients per strategy controls the number of simulated individuals in each arm. The current workflow uses common state-level parameters for all individuals; it does not accept patient-specific risks or history-dependent transition rules.
Use the seed under Reproducibility & notes to repeat a run, and vary it when checking numerical stability. Patients times cycles may not exceed 20 million per strategy. Start with a feasible population, inspect precision, and increase it only as needed.
Input reference (12 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 |
|---|---|---|---|
| Health states | Text | — | Names correspond to transition rows and columns. Add one name or comma-separated names in the State manager. It creates matching matrices for both strategies. |
| Comparator transitions | Table | — | Each row must sum to 1. Probabilities apply to one model cycle. Rows and columns follow the order in the State manager. Fill every cell, including zeros, before saving or running. |
| Intervention transitions | Table | — | Use the same state order as the comparator. Rows and columns follow the order in the State manager. Fill every cell, including zeros, before saving or running. |
| Annual state rewards | Table | — | Costs per year and utilities; the engine applies cycle length. Columns in order: Comparator cost; Comparator utility; Intervention cost; Intervention utility. |
| Initial state proportions | Text | — | One proportion per state, summing to 1. |
| Number of cycles | Whole number | 1–500 | — |
| Cycle length (years) | Number | 0.01–10 | — |
| Annual cost discount rate | Number | 0–0.2 | — |
| Annual QALY discount rate | Number | 0–0.2 | — |
| State reward timing | Selection | — | Choices: trapezoid, start. |
| Willingness to pay per QALY | Number | 0–10000000 | — |
| Patients per strategy | Whole number | 100–100000 | — |
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
Strategy outcomes and Incremental comparison use simulated means. Monte Carlo precision reports each strategy/outcome mean, Monte Carlo standard error, 95% simulation interval, and Cohort expectation.
The occupancy tables remain the deterministic cohort traces for the same parameters; they are not counts from the simulated individuals. Use the cohort expectation as a numerical benchmark. The intervals describe simulation error under fixed inputs, not confidence in treatment effectiveness or uncertainty in model parameters.
Troubleshooting and common mistakes
If an operation-limit error occurs, reduce patients or cycles while preserving a scientifically appropriate horizon. Do not shorten follow-up solely to make the run complete without recording the changed question.
If repeat seeds produce visibly different incremental results, increase patients and inspect Monte Carlo precision. If means remain far from cohort expectations, recheck rewards and timing before increasing the run further. Rare-event instability and an incorrect model need different remedies.
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.