{
  "annotation_id": "57e3ebd9-3ce5-4cc4-8c75-c2b45570b027",
  "date_created": "2026-02-17T05:53:08.859000Z",
  "date_modified": "2026-02-17T05:53:08.859000Z",
  "file_hash": "1dd4acf8bd43662f0dc112bd1b1572d2f348bd6c8f5f436f23f1d60001020341",
  "private": false,
  "record": {
    "abstract": "A targeted learning (TL) framework is developed to estimate the difference in the restricted mean survival time (RMST) for a clinical trial with time-to-event outcomes. The approach starts by defining the target estimand as the RMST difference between investigational and control treatments. Next, an efficient estimation method is introduced: a targeted minimum loss estimator (TMLE) utilizing pseudo-observations. Moreover, a version of the copy reference (CR) approach is developed to perform a sensitivity analysis for right-censoring. The proposed TL framework is demonstrated using a real data application.",
    "arxiv_id": "2601.06296",
    "authors": [
      "Man Jin",
      "Yixin Fang"
    ],
    "categories": [
      "stat.ME",
      "stat.ML"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "A Framework for Estimating Restricted Mean Survival Time Difference using Pseudo-observations",
    "url": "https://arxiv.org/abs/2601.06296",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "63a873f1-3b06-4ea5-9283-c5240517030c",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}