Synthetic Data for Pharma and Research
Clinical research data is longitudinal and stratified: repeated measurements per subject, distributions that differ by trial arm. Misata generates visits over ordered time, varies outcome distributions by arm through stratified profiles, and keeps every measurement tied to a real subject.
The tables Misata generates
trialsStudy names, phases, armssubjectsEnrollment, arm assignment, visit-count roll-upsvisitsScheduled over ordered time per subjectmeasurementsOutcomes with distributions stratified by trial armWhat holds true, every time
- Outcome distributions differ by arm, as a real trial would show
- Visits are ordered in time within each subject
- Every measurement and visit references a real subject
- No real subject data is sourced or leaked
Frequently asked
Do I need real pharma data to generate this?
No. Misata builds the dataset from a specification, not a sample. There is no real pharma data to source, anonymize, or leak. You describe the tables you need and the engine constructs them with referential integrity and realistic distributions.
Is the generated pharma data privacy safe?
Yes, by construction. Nothing is learned from real records, so there is no membership to infer and nothing to leak. It runs entirely on your machine with no API key for the core engine.
Can I control the outcomes, like rates and totals?
Yes. Declare a target such as a monthly volume curve or an event rate and Misata produces rows that hit it exactly, while foreign keys stay intact and roll-up columns reconcile after a JOIN.
Choosing a tool? How Misata compares