Grounded in real work,
built to be inspected.
We earn trust the same way we build evidence: by being precise, by showing our work, and by keeping raw data where it belongs. This page states plainly how data is handled, how the method is validated, and which guidance we answer to.
Four things a careful reader can check.
Built to the standard your data already lives under.
De-identification, access control, and provenance are handled inside your existing HIPAA framework. We operate within it, we do not ask you to work around it.
Methods that answer to published guidance.
Cohort assembly and endpoint derivation follow FDA guidance on externally controlled trials and on AI in regulatory decision-making, with EMA precedent behind the approach.
Grounded in real work with real institutions.
Active data partnerships with clinical partners in the US and India. Per our naming rule, we describe partners by type, not by name, until they choose otherwise.
Every endpoint traces back to its source record.
A derived scale is never a black box. Each item links to the exact sentence, value, or measurement it came from, so a biostatistician can inspect the provenance of every point.
Data is de-identified at source, and structured in place.
No raw record leaves the institution that holds it. Here is what that means step by step, in plain language.
- 01
De-identified at source
Identifiers are removed before any processing begins, inside the partner's environment. Sapien never receives an identifiable record.
- 02
Structured in place
The de-identified data is structured and harmonized where it already lives. It is analysis-ready on the partner's own servers, not on ours.
- 03
Accessed through a governed key
Sapien works through a governed research key with defined access and provenance. Governance is agreed with the partner before any data moves.
- 04
Only evidence crosses the boundary
What leaves is a de-identified, structured evidence package, traceable back to its source for audit. Raw records never leave the partner's environment.
Because identifiers are removed at source and raw records stay inside the partner’s governance boundary, processing sits within existing HIPAA, GDPR, and India’s DPDP frameworks rather than working around them. Governance, access, and provenance are defined with the partner before any data moves. Every output can be traced to a source record for audit, while the identifiers that would make it personal data were never present in what Sapien handles.
The guidance the method answers to.
Public regulatory references, not our partners. These frame how an external control arm is designed, validated, and defended.
Externally Controlled Trials for Drug and Biological Products
The FDA draft guidance on the design and conduct of externally controlled trials. The reference frame for how an external control arm is built and defended.
Use of AI to Support Regulatory Decision-Making
Introduces a seven-step credibility-assessment framework for AI used in regulatory submissions. Our validation and traceability are designed against it.
PROCOVA qualification
EMA qualified a covariate-adjustment method for sample-size reduction in Phase 2 and 3 trials with continuous outcomes. Precedent that regulators engage with model-based control methods.
Choice of Control Group in Clinical Trials
The international guideline on selecting a trial control group, including external controls. The long-standing basis for when an external comparator is appropriate.
The best way to trust it
is to inspect it.
We would rather walk your team through the provenance of a single endpoint than make a claim you cannot check. Book a working session and bring your hardest question.