{
  "abstract": "Competing risks analysis is an important framework for studying time-to-event data in the presence of multiple, mutually exclusive events. Traditional methods, such as non-parametric estimators and proportional hazards models, may not adequately capture complex hazard patterns or provide stable estimates in practical settings. In this study, we propose a parametric approach based on the generalized exponential regression model for competing risks data. The model allows flexible hazard shapes and incorporates covariate effects through a regression structure. To improve estimation stability, we integrate elastic net regularization into the likelihood framework. The proposed method is evaluated through simulation studies and applied to a real dataset. The results indicate that the elastic net approach provides more stable and precise estimates compared to standard maximum likelihood estimation. Overall, the proposed framework offers a flexible and practical approach for analyzing competing risks data.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.37235",
  "author_total": 1,
  "authors": [
    {
      "name": "Avinash Kumar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1bs306ydj55zd.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.37235",
  "fulltext_url": null,
  "id": "1bs306ydj55zd",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.37235",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Inference"
    ]
  ],
  "openalex": "W7224088529",
  "pdf_url": "https://arxiv.org/pdf/2610.37235",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1bs306ydj55zd.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:35:59Z",
      "native_id": "2610.37235",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.37235"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Penalized Cause-Specific Hazard Regression under Competing Risks: An Elastic Net Approach Using SANAD Trial Data",
  "title_text": "Penalized Cause-Specific Hazard Regression under Competing Risks: An Elastic Net Approach Using SANAD Trial Data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:35:59Z",
  "url": "https://research.draconitic.com/paper/1bs306ydj55zd",
  "venue": "arXiv",
  "versions": []
}