| 1. Confirm the biomarker definition | Endpoint and measurement method | A precise definition of the biomarker, specimen or signal type, assay or collection method, unit of measurement, and intended use. | Match the supplier’s data dictionary against the buyer’s study protocol and analytical objective. | The same label is used for different biological signals, endpoints, or measurement units across markets. |
| 2. Compare analytical performance | Accuracy, precision, sensitivity, and specificity | Validation evidence should identify the reference method, tested population, limit of detection where relevant, repeatability, reproducibility, sensitivity, and specificity. | Request validation reports and verify that performance results apply to the same population and use case. | Only a single accuracy percentage is provided without confidence intervals, sample size, comparator method, or population details. |
| 3. Test cross-market comparability | Population, geography, and collection context | Country, region, age range, sex or gender variables, ethnicity where ethically and legally appropriate, inclusion criteria, and collection period are documented. | Check whether observed differences may result from sampling, healthcare access, diet, environment, or collection procedures rather than the biomarker itself. | Regional datasets are pooled without stratification, weighting, or explanation of demographic imbalance. |
| 4. Review pre-analytical controls | Collection, transport, storage, and processing | Standard operating procedures cover collection time, fasting status where applicable, temperature control, time to processing, storage duration, freeze-thaw cycles, and missing-data handling. | Compare procedures between countries and require documented deviation logs. | Samples from different markets use inconsistent handling conditions or have undocumented storage histories. |
| 5. Verify laboratory and data quality | Quality management and auditability | The provider can demonstrate documented quality procedures, instrument calibration, proficiency testing where applicable, version-controlled methods, and traceable audit records. | Prefer evidence aligned with recognized laboratory or biobank quality frameworks, such as ISO 15189 or ISO 20387, when relevant to the service. | Results cannot be traced to a method version, instrument run, operator, or quality-control record. |
| 6. Assess privacy and transfer controls | Consent, lawful basis, and international data transfer | The data package identifies consent or other lawful basis, de-identification or pseudonymization controls, permitted uses, retention period, access controls, and cross-border transfer mechanisms. | Obtain country-specific privacy documentation and confirm that secondary use and international transfer are authorized. | The supplier cannot explain whether data are identifiable, how consent limits use, or where the data are stored. |
| 7. Require reproducible delivery and monitoring | Data format, metadata, updates, and change control | Files include stable identifiers, units, timestamps, missing-value codes, method versions, metadata, validation rules, and a documented process for correcting or updating records. | Run a pilot reconciliation and define acceptance thresholds for completeness, duplicate records, invalid values, and delivery timeliness. | Schema changes, corrected values, or missing records are delivered without version history or notification. |