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Surrogate endpoints Validation study applicable to the disease, its severity, the intervention, and the comparator required (exception: very serious diseases).
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Widely accepted statistical methodology for surrogate endpoint validation.
Substantive discussions of surrogate endpoint validation began in the late 1980s and early 1990s partly driven by the need to find valid biomarkers for Acquired Immunodeficiency Syndrome (AIDS) randomised controlled trials.
In this summary, information from published studies is used as a basis to critique clinical trial designs and to suggest experimental endpoints for future validation studies.
A total of 100 validation endpoints were simulated across treatment arms of twelve pivotal T2DM outcomes studies.
In 90% of validation endpoints, sampled values within 2 standard errors of the mean (as reported by UKPDS) would have resulted in predicted endpoints lying on the 45º identity line.
The primary clinical endpoints in the validation datasets include disease-specific survival (DSS), disease-free survival (DFS), distant metastasis-free survival (DMFS), overall survival (OS), relapse-free survival (RFS), and distant relapse-free survival (DRFS).
A total of 100 validation endpoints were simulated across treatment arms of twelve pivotal T2DM outcomes studies, simulation cohorts representing each validation study's cohort profile were generated and intensive and conventional treatment arms were defined in the Cardiff Diabetes Model.
Training dataset contains 130 samples (33 positives and 97 negatives for endpoint D, 80 positives and 50 negatives for endpoint E), and validation dataset contains 100 samples (15 positives and 85 negatives for endpoint D, 61 positives and 39 negatives for endpoint E).
Figure 1 illustrates the relationship between study observed versus predicted endpoints stratified by validations study, endpoint and UKPDS equations.
Upper and lower ranges are shown for each risk factor exerting most influence on prediction validation endpoint.
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