dicentra applies a staged validation framework published in:
This framework integrates analytical validity, clinical validity, and clinical utility into a deliberate regulatory and reimbursement roadmap. Rather than treating validation milestones as isolated checkpoints, we design studies that intentionally generate parallel evidence across regulatory submission and payer objectives.
By aligning statistical design, regulatory planning, and clinical operations early, we help reduce late-stage evidence gaps and improve submission efficiency.
Analytical Validity — Can the test measure reliably?
Analytical validity evaluates measurement performance under controlled conditions. Key elements include bias (closeness to reference), imprecision (coefficient of variation), limit of detection (LOD), linearity, interference, and lot-to-lot consistency. Bench studies and contrived samples are typically used to establish these characteristics.
Clinical Validity — Does the test correctly classify patients?
Clinical validity establishes whether the test result accurately identifies disease or clinical state within the intended-use population. Performance metrics may include sensitivity, specificity, positive and negative percent agreement (PPA/NPA), predictive values, ROC/AUC analysis, and agreement statistics.
Clinical Utility — Does using the test improve care?
Clinical utility assesses whether implementing the test changes clinical decisions and leads to improved patient outcomes or health-system efficiency. Endpoints may include time-to-treatment, length of stay, readmission rates, workflow efficiency, and health-economic impact.
dicentra ensures that each validation study clearly defines its objective, specimen source (contrived versus clinical), and statistical plan so regulators interpret results in the appropriate analytical or clinical context.
Our multidisciplinary teams align clinical operations, biostatistics, regulatory affairs, human factors expertise, and health economics to reduce downstream surprises and accelerate market access. By coordinating analytical performance studies, multicenter clinical trials, and utility modeling within a unified framework, we help ensure that promising POC technologies reach patients faster — supported by robust, regulator-ready, and payer-aligned evidence.
These publications demonstrate hands-on execution of diagnostic validation, bias mitigation, statistical modeling, and outcome-focused evaluation supporting both regulatory and reimbursement pathways.
Analytical sensitivity refers to the lowest measurable concentration detectable under controlled laboratory conditions (limit of detection). Clinical sensitivity reflects how accurately the test identifies disease in patients within the intended-use population.
Yes. A well-designed multicenter prospective diagnostic accuracy study can simultaneously collect usability data, short-term outcomes, and health-economic endpoints when planned intentionally.
Clinical utility endpoints are most efficiently embedded during or immediately following clinical validity studies to align regulatory and reimbursement objectives.
Yes. We design decentralized studies addressing operator variability, usability, and intended-use environments consistent with CLIA waiver requirements.
Yes. We support statistical validation, real-world performance evaluation, change control strategy planning (PCCP), and post-market evidence development for AI-driven diagnostics.