{
  "id": 18154434,
  "title": "KDRD: assessing discovery replicability based on summary association statistics from a single study",
  "url": "https://urgent.news/2026/11/04/kdrd-assessing-discovery-replicability-based-on-summary-association",
  "topic": "science",
  "section": "Science",
  "published": "2026-11-04T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.11.03.763861v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Replicability is essential for reliable discoveries in large-scale association studies. However, conventional replicability assessments typically require independent datasets or repeated data splitting, which may be constrained by data availability, computational burden and reduced sample sizes. Here, we developed Kernel Density-based Replicability of Discovery (KDRD), a statistical framework for…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}