Outcome-based analytical performance specifications: current status and future challenges.

Analytical performance specifications (APS) based on outcomes refer to how 'good' the analytical performance of a test needs to be to do more good than harm to the patient. Analytical performance of a measurand affects its clinical performance. Without first setting clinical performance requirements, it is difficult to define how good analytically the test needs to be to meet medical needs. As testing is indirectly linked to health outcomes through clinical decisions on patient management, often simulation-based studies are used to assess the impact of analytical performance on the probability of clinical outcomes which is then translated to Model 1b APS according to the Milan consensus. This paper discusses the related key definitions, concepts and considerations that should assist in finding the most appropriate methods for deriving Model 1b APS. We review the advantages and limitations of published methods and discuss the criteria for transferability of Model 1b APS to different settings. We consider that the definition of the clinically acceptable misclassification rate is central to Model 1b APS. We provide some examples and guidance on a more systematic approach for first defining the clinical performance requirements for tests and we also highlight a few ideas to tackle the future challenges associated with providing outcome-based APS for laboratory testing.

Applying the Milan models to setting analytical performance specifications - considering all the information.

Analytical performance specifications (APS) are used for decisions about the required analytical quality of pathology tests to meet clinical needs. The Milan models, based on clinical outcome, biological variation, or state of the art, were developed to provide a framework for setting APS. An approach has been proposed to assign each measurand to one of the models based on a defined clinical use, physiological control, or an absence of quality information about these factors. In this paper we propose that in addition to such assignment, available information from all models should be considered using a risk-based approach that considers the purpose and role of the actual test in a clinical pathway and its impact on medical decisions and clinical outcomes in addition to biological variation and the state-of-the-art. Consideration of APS already in use and the use of results in calculations may also need to be considered to determine the most appropriate APS for use in a specific setting.

Validity and Efficiency of Progression-Free Survival-2 as a Surrogate End Point for Overall Survival in Advanced Cancer Randomized Trials.

PURPOSE: Progression-free survival (PFS)-2, defined as the time from randomization to progression on second-line therapy, is potentially a more reliable surrogate than PFS for overall survival (OS), but will require longer follow-up and a larger sample size. We sought to compare the validity and efficiency, defined as proportional increase in follow-up time and sample size, of PFS-2 to PFS. METHODS: We performed an electronic search to identify randomized trials of advanced solid tumors reporting PFS, PFS-2, and OS as prespecified end points. Only studies that had protocols that defined measurement of PFS-2 and follow-up for patients after first disease progression were included. We compared correlations in the relative treatment effect for OS with PFS and PFS-2. We reconstructed individual patient data from survival curves to estimate time to statistical significance (TSS) of the relative treatment effect. We further computed the sample size (person-year [PY] follow-up) required to reach statistical significance. RESULTS: Across the 42 analysis units and 21,255 patients, the correlation of the relative treatment effect between OS and PFS-2, r, was 0.70 (95% CI, 0.41 to 0.80) and r = 0.46 (95% CI, 0.26 to 0.74) for OS and PFS. The median differences in TSS between OS with PFS, OS with PFS-2, and PFS with PFS-2 were 16.59 (95% CI, 4.48 to not reached [NR]), 10.0 (95% CI, 2.2 to NR), and 4.31 (95% CI, 2.92 to 13.13) months, respectively. The median difference in PYs required to reach statistical significance for PFS-2 over PFS was 156 (95% CI, 82 to 500) PYs, equivalent to an estimated median 12.7% increase in PYs. CONCLUSION: PFS-2 offers improved correlation with OS than PFS with a modest increase in follow-up time and sample size. PFS-2 should be considered as a primary end point in future trials of advanced cancers.