where now F0 and F1 are the cumulative distributions. For known π0 and F1 and large number of multiple tests, the FPRP is the same as the q-value [56], the main difference being one of context. FPRP is intended to be applied across multiple studies and calculated from prior models, whereas q-values are motivated by the within-study FDR and are usually estimated from data. FPRP is also mathematically complementary to the positive predictive value of a discriminant [64], again differing in context. Because FPRP is a property of a range of test statistics, it is appropriate for setting guidelines for the reporting of significant results, based on assumed models for π0 and F1. This means that results can continue to be reported according to their p-values, but with modified thresholds of significance. A known proportion of reported results will then be false; however, for assessment of specific tests for follow-up, the local FDR is more relevant to investigators.
