Classification-Specific Predictive Performance: A Unified Estimation and Inference Framework for Multi-Category Tests

Multi-cancer testing with localization aims to detect signals from any of a set of targeted cancer types and predict the cancer signal origin from a single biological sample. Such tests have the potential to aid clinical decisions and significantly improve health outcomes. When used for multi-cancer screening in an asymptomatic population, these tests are referred to as multi-cancer early detection (MCED) tests. MCED testing has not yet achieved regulatory approval, reimbursement or broad clinical adoption. Some major reasons for this are that the clinical benefits and harms are not well understood, including the risk of unnecessary work-ups and false reassurance from a negative test that could reduce uptake of standard-of-care screening. Part of this uncertainty stems from the use of clinically obtuse metrics to assess the test's clinical validity. Traditionally, performance of MCED tests has been quantified using aggregate measures, disregarding the joint distribution of cancer type, stage (both at intended-use incidence rates) and predicted cancer signal origin, thereby obscuring biological variability and underlying differences in the test's behavior and limiting insight into true effectiveness. Clinically informative evaluation of an MCED test's performance requires metrics that are specific to cancer type, stage and predicted cancer origin at expected incidence rates in the intended-use population. In the context of a case-control sampling design, this paper derives analytical methods that allow for unbiased estimation of cancer-specific intrinsic accuracy, predicted cancer signal origin-specific predictive value and the marginal test classification distribution, each with corresponding valid confidence interval formulae. A simulation study is presented that evaluates performance of the proposed methodology and provides guidance for implementation. An application to a published MCED test dataset is given. The derived statistical analysis framework in general allows for estimation and inference for pointed metrics of a multi-category test that enables precisely informed decision-making, supports optimized trial designs across classical, digital, AI-driven, and hybrid stratified diagnostic screening platforms, and facilitates informed healthcare decisions by clinicians, policymakers, regulators, scientists, and patients.