The Role of Clinical-Translational Studies in the Validation of Diagnostic Devices
Clinical-translational studies represent the critical inflection point where a diagnostic technology transitions from a laboratory assay to a validated clinical asset. Understanding the distinction between translational research and formal clinical investigations, and how they sequence together, is essential for designing an efficient regulatory and statistical strategy.
Navigating the Diagnostic Device Landscape
Before examining the translational phase, it is vital to distinguish between the two primary categories of diagnostic devices, as they are governed by different regulatory frameworks:
- In Vitro Diagnostics (IVDs): Devices that examine specimens derived from the human body (e.g., molecular assays, lateral flow tests). These are regulated in the EU under the IVDR (2017/746) and internationally by ISO 20916 for clinical performance studies.
- In Vivo Diagnostic Devices: Devices used to scan, image, or monitor the living body (e.g., AI-imaging software, continuous glucose monitors, ECG devices). In the EU, these are regulated under the Medical Device Regulation (MDR 2017/745) and internationally by ISO 14155 for clinical investigations.
Despite these regulatory differences, both categories share a fundamental developmental pipeline: moving from analytical/technical validity, to clinical validity, and finally to clinical utility.
Translational Studies vs. Clinical Investigations
A translational study does not “turn into” a clinical investigation. Rather, they are sequential, distinct phases.
- Clinical-Translational Studies (Clinical Validity): These studies focus on proving that the device or assay can accurately detect or predict a specific clinical condition in the intended-use population. For IVDs, this might involve retrospective testing of biobanked specimens. For in vivo devices (like AI imaging), this might involve a retrospective analysis of previously acquired patient scans. These studies establish the foundational metrics of diagnostic accuracy but do not dictate real-time patient management.
- Clinical Investigations (Clinical Utility): These are formal, prospective trials where the device is used in real-time to guide actual patient care. The goal is to prove that using the test changes clinical decisions and meaningfully improves health outcomes compared to the standard of care.
The clinical validity data generated in the translational phase acts as the statistical and scientific justification for initiating a formal clinical investigation. If the translational study fails to demonstrate sufficient accuracy, a prospective investigation is unjustified.
Core Components of Clinical-Translational Studies
1. Establishing the Statistical Foundation for Investigation While sensitivity and specificity are the foundational metrics of clinical validity, they are prevalence-independent measures. Translational studies employ comprehensive biostatistical analyses, including Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) calculations, to evaluate the trade-off between sensitivity and specificity across all possible cut-off points.
Crucially, this analysis allows a sponsor to “lock” the diagnostic threshold (e.g., a specific biomarker concentration for an IVD, or a pixel-density threshold for an AI imaging device) before entering a prospective clinical investigation. Changing a threshold mid-trial introduces severe regulatory bias. Translational studies also calculate Positive Predictive Values (PPV) and Negative Predictive Values (NPV) to reflect the test’s performance in the context of actual disease prevalence, ensuring the subsequent investigation is powered correctly.
2. Mitigating Spectrum Bias and Ensuring Generalisability A frequent pitfall in early translational research is spectrum bias, which occurs when the study population does not accurately reflect the intended-use population. Testing a device exclusively on severe cases and healthy controls will artificially inflate its apparent accuracy.
Translational studies must be designed to include a broad spectrum of patients encompassing varying demographics, comorbidities, and disease severities. Biostatisticians employ stratified analyses and multivariable regression models to identify potential variations in test performance across subgroups. This ensures that when the novel diagnostic finally enters a clinical investigation, the inclusion/exclusion criteria are statistically sound and generalisable to real-world settings.
3. Handling Imperfect Reference Standards A major statistical challenge in translational diagnostics is the lack of a perfect “gold standard.” If the existing standard of care is flawed, comparing a new device to it will produce biased accuracy estimates.
Translational studies often utilise advanced statistical modelling to handle this, using methods such as composite reference standards (CRS) or latent class models. Resolving these reference standard issues during the translational phase is critical; if left unresolved, they can invalidate the endpoints of a future clinical investigation.
4. Preparing for Real-World Evidence (RWE) and Longitudinal Monitoring While translational studies are not full clinical investigations, they must be designed with future regulatory expectations in mind. Under both the EU IVDR (Article 79) and MDR, Post-Market Clinical Follow-up (PMCF) or Post-Market Performance Follow-up (PMPF) are regulatory requirements.
Early integration of RWE methodologies during the translational phase involves using advanced statistical techniques to control for confounding in routine clinical data. This helps sponsors anticipate algorithmic drift (particularly in AI/ML diagnostics). For diagnostics intended for monitoring disease progression, longitudinal translational designs using mixed-effects models are necessary to prove the device can track clinical changes over time before a costly interventional investigation begins.
The Biostatistical Imperative
The complexity of modern diagnostic devices demands biostatistical involvement from the outset of the translational phase. Biostatisticians are essential for calculating precise sample sizes (e.g., using Buderer’s formula for sensitivity/specificity precision based on desired confidence interval widths), designing retrospective or cross-sectional cohorts that minimise bias, and resolving reference standard ambiguities.
By establishing unassailable clinical validity through robust translational studies, sponsors ensure they have the statistical evidence required to justify and design a successful clinical investigation – ultimately accelerating the path to market and patient access.
For more information on our services for clinical-translational studies see here.
If you are designing a clinical-translational study to establish the clinical validity of your diagnostic device or IVD, the statistical framework must be robust from day one. Contact Anatomise Biostats to discuss how our team can support your study design, sample size calculations, and regulatory submission strategy.
References & Regulatory Frameworks
- European Union. (2017). Regulation (EU) 2017/746 on In Vitro Diagnostic Medical Devices (IVDR). (Specifically Article 70 for Clinical Performance Studies, and Article 79 for Post-Market Performance Follow-up).
- European Union. (2017). Regulation (EU) 2017/745 on Medical Devices (MDR). (Applies to in vivo diagnostic devices, imaging equipment, and AI software; see Chapter VI for Clinical Investigations).
- International Organization for Standardization (ISO). (2019). ISO 20916:2019 – In vitro diagnostic medical devices — Clinical performance studies using specimens from human subjects — Good study practice.
- International Organization for Standardization (ISO). (2021). ISO 14155:2021 – Clinical investigation of medical devices for human subjects — Good clinical practice.
- U.S. Food and Drug Administration (FDA). (2007). Guidance for Industry and FDA Staff: Statistical Guidance on Reporting Results from Studies Evaluating Diagnostic Tests. (Covers the statistical principles of ROC curves, spectrum bias, and PPV/NPV reporting).
- Buderer, N. M. (1996). Statistical methodology: I. Incorporating the prevalence of disease into the sample size calculation for sensitivity and specificity. Academic Emergency Medicine, 3(9), 895-900. (The foundational formula for diagnostic sample size calculation based on precision).
- Rutjes, A. W., Reitsma, J. B., Coomarasamy, A., Khan, K. S., & Bossuyt, P. M. (2007). Evaluation of diagnostic tests when there is no gold standard: A review of methods. Health Technology Assessment, 11(50). (Details the use of Composite Reference Standards and latent class models).
