[DeepGOF] Where Does a Logistic Risk Model Fail? An Audited Neural Goodness-of-Fit Test for Model Development and External Validation

Goodness-of-fit tests for logistic regression are routine in clinical risk modelling, yet the classical tests say whether a model misfits, not where. A pretrained network is not a test until its level, power and blind spots are established. We audit DeepGOF-1, which renders the residuals of a fitted logistic model as a map over the ranks of two covariates, scores the map with a frozen convolutional network, and calibrates the score by the analyst's parametric bootstrap. The level is first-order valid under standard bootstrap regularity and two conditions, consistency against a named alternative is decided by one forward pass, and local power has an explicit limit. In simulation the level holds from 20 patients to 8,873; the network adds power over a chi-square statistic of the same map when misfit is sparse among many covariates; the map finds a missed interaction that one-covariate diagnostics cannot see; and, applied to a published risk model on new patients, it is an exactly valid test of calibration within patient subgroups, where the calibration belt has little power. In the SUPPORT study of 8,873 inpatients, DeepGOF-1 rejects a linear in-hospital mortality model, its map shows the U-shaped risks of blood pressure and respiratory rate that a calibration curve hides, and it guides the repairs. It ran in 13 seconds; the projection test, the strongest omnibus rival, took 9 hours 53 minutes and 4.5 GB on the same data, too slow for routine use. The test is deepgof1() in the R package ebrahim.gof.