{
  "abstract": "The average hazard (AH) is a recently proposed model-free summary of an event-time distribution. Over a prespecified window $[0,τ]$, it equals the cumulative incidence probability at $τ$ divided by the restricted mean survival time at $τ$ and can be interpreted as a person-time incidence rate that does not depend on the censoring distribution. The difference and ratio of AHs from two groups, DAH and RAH, are between-group contrasts on absolute and relative scales without requiring proportional hazards. Existing nonparametric inference for AH and its contrasts is based on the Kaplan--Meier estimator of the survival function, and can become unreliable when risk sets become sparse or exhausted near $τ$, as may occur in stratified analyses. As a practical remedy, we investigate parametric AH inference using Weibull and generalized gamma models. We use maximum-likelihood estimators and corresponding confidence intervals for group-specific AHs, DAH and RAH in both unstratified and stratified analyses. Simulations show that under correct model specification, little bias and near-nominal coverage can be achieved in settings where nonparametric inference is inadequately supported by observed data. We illustrate the method using a randomized trial comparing neratinib plus capecitabine with lapatinib plus capecitabine in metastatic breast cancer. Parametric inference provides a practical complement to nonparametric AH analysis when sparse or exhausted risk sets make inference at clinically relevant truncation times less reliable.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.41935",
  "author_total": 3,
  "authors": [
    {
      "name": "Zihan Qian"
    },
    {
      "name": "Lu Tian"
    },
    {
      "name": "Hajime Uno"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2d41j9s23rsxm.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.41935",
  "fulltext_url": null,
  "id": "2d41j9s23rsxm",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.41935",
  "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.ME"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.41935",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2d41j9s23rsxm.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:25Z",
      "native_id": "2610.41935",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.41935"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Parametric inference for the average hazard approach in time-to-event analysis with sparse risk sets",
  "title_text": "Parametric inference for the average hazard approach in time-to-event analysis with sparse risk sets",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:53Z",
  "url": "https://research.draconitic.com/paper/2d41j9s23rsxm",
  "venue": "arXiv",
  "versions": []
}