arXiv subjects

Osama Abd El-Aziz Hussein

Publications and source records attributed to Osama Abd El-Aziz Hussein.

3 recordsLinked to original sources

arXiv2026

One Goodness-of-Fit Test Is Not Enough: Error-Controlled Localization of Misfit in Logistic Risk Models

A goodness-of-fit test tells whether a logistic risk model fits, but the analyst also needs to know what is wrong: an overall risk that is too high is repaired by updating the intercept, risks that are too extreme by recalibration, a curved relation between score and risk by a flexible recalibration, and misfit within groups of patients only by revising the model. We split any misfit into these four parts, which are orthogonal and match the calibration hierarchy. Each part gets a group of score tests that sees that part only, and the failing parts are named by closed testing. In external validation, in large samples and for small departures, a part is wrongly named with probability at most the chosen level, and the highest part named points to the lightest update that removes the misfit. Checking a model on its development data never sees an error in the overall risk or the calibration slope, and with binary or skewed covariates it can mistake misfit within patient groups for a curved link. External validation sees all four parts, for any prediction model. In simulations it kept its error rate when the model was right and for small departures, and usually pointed to the right update. On intensive-care data its verdicts for one model differed in-sample, on similar patients and after transport.

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arXiv2026

[EDGE] A grouped calibration test for logistic regression that tolerates a few corrupted records

The Hosmer-Lemeshow calibration test groups patients by predicted risk for a valid reference and loses power that more groups cannot recover. Later tests weight each record's residual alone, which makes them fragile: on an otherwise correct model, one record in a thousand with a corrupted covariate raises the false-alarm rate of Stukel's score test from 5% to 12%, and ten to 63%. Pooling on equal-size groups bounds a wrong record's influence, dividing its residual by its groupmates' variance; without pooling, the same directions are as fragile as Stukel's test after a refit. EDGE, the test proposed here, projects the grouped residuals onto a small basis of calibration shapes. Its degrees of freedom do not depend on the number of groups, so the partition becomes a setting, fine-grained for power and coarse when records may be wrong, and its reference needs no resampling. In a prespecified simulation EDGE was comparable in power to Stukel's test on clean data and 0.064 more powerful on average than the ten-group Hosmer-Lemeshow test. With one and ten exaggerated covariates in a thousand its false-alarm rates were 0.056 and 0.097, and with ten groups below 0.10 up to about ten exaggerated or five reversed. In external validation, at ten groups, its average power was that of Cox's recalibration test and the GiViTI belt, and its false-alarm rate stayed below 0.10 with ten reversed records in a thousand, where theirs did not. In a 70,000-patient cohort its verdict did not change with the partition.

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arXiv2026

[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.

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