Diagnostic, Prognostic, and Disease Monitoring Algorithms: What They Are and How Their Statistical Validation Differs
AI Algorithms now sit at every point in the patient pathway. They identify who has a disease, forecast what will happen next, and track change over time. The statistical machinery needed to validate each of these three types is fundamentally different. A model that classifies a patient as "disease-positive" at a single timepoint is not validated the same way as one that predicts 5-year survival. Neither maps neatly onto…
Quick Guide: Simulation Evidence for Frequentist Adaptive Designs in Medtech Clinical Trials
A fixed frequentist design, such as a standard two-arm randomised superiority study, generally does not require simulation. Its statistical properties come from established formulas. Closed-form or table-based power calculations tell you the sample size, and the Type I error rate follows from the test you specified. Simulation enters the picture when the study design gets messy in ways the standard formulas can't absorb, for instance complex survival endpoints, clustered…
High-Dimensional Data and Machine Learning for SaMD (Software as a Medical Device)
Series: Advanced Biostatistics for MedTech: Bridging Clinical Evaluation and Engineering The Regulatory Shift Toward Algorithmic Validation The integration of machine learning (ML) into Software as a Medical Device (SaMD), has fundamentally altered the regulatory landscape. The industry’s transition from the use of traditional statistical modeling (SM) to machine learning (ML) within device technology, has necessitated a paradigm shift in how predictive algorithms are validated. Under the EU Medical Device…