{
  "abstract": "Incomplete adjudication and endpoint misclassification arise when gold-standard event classification is available only for an internal validation sample. With accurate follow-up times and covariates but error-prone event indicators, naive Cox regression can be biased and inverseprobability weighting inefficient. We develop orthogonal validation-augmented Cox (OVAC) regression, a cross-fitted augmented Cox estimator for incomplete failure indicators. OVAC replaces the missing event indicator with a cross-fitted augmented pseudo-event and explicitly retains the first-order contribution from estimating the Cox risk-set mean. The same term emerges from projection of the full-data influence function through the validation mechanism, linking the score expansion to the observed-data efficient influence function. The score exhibits exact product-form nuisance drift, yielding double-robust identification, Neyman orthogonality, and root-n inference under product-rate conditions. In 1,000 R replications, full-variance SE/SD ratios were 0.997 and 0.992, with 95% coverage of 0.955 and 0.948. A 200-replication diagnostic showed that the risk-set term alone was 77.8% and 81.1% of the full-SE magnitude, although covariance cancellation made its net SE effect smaller than 0.3%. In a 250-replication method comparison, OVAC reduced empirical variance by 38.8% and 37.7% relative to IPW. OVAC provides machine-learning-compatible Cox estimation with explicit risk-set linearization, calibrated inference, and validation-design guidance.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2608.28839",
  "author_total": 1,
  "authors": [
    {
      "name": "Subir Hait"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8s7zzpk9k0c6m.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.28839",
  "fulltext_url": null,
  "id": "8s7zzpk9k0c6m",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.28839",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Inference"
    ]
  ],
  "openalex": "W7204936557",
  "pdf_url": "https://arxiv.org/pdf/2608.28839",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8s7zzpk9k0c6m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:54Z",
      "native_id": "2608.28839",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.28839"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Orthogonal Validation-Augmented Cox Regression with Internally Validated Failure Indicators Cross-Fitted Estimation, Risk-Set Linearization, and Validation Design",
  "title_text": "Orthogonal Validation-Augmented Cox Regression with Internally Validated Failure Indicators Cross-Fitted Estimation, Risk-Set Linearization, and Validation Design",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:54Z",
  "url": "https://research.draconitic.com/paper/8s7zzpk9k0c6m",
  "venue": "arXiv",
  "versions": []
}