{
  "annotation_id": "db772c0e-5378-418d-9c21-5aa7299e6c07",
  "date_created": "2026-02-17T05:53:12.601000Z",
  "date_modified": "2026-02-17T05:53:12.601000Z",
  "file_hash": "e5099283eae9442e1ae28abc7da8d200ec49baf12511aafaeab6e2378f50c014",
  "private": false,
  "record": {
    "abstract": "Indirect treatment comparisons (ITCs) are essential in Health Technology Assessment (HTA) when head-to-head clinical trials are absent. A common challenge arises when attempting to compare a treatment with available individual patient data (IPD) against a competitor with only reported aggregate-level data (ALD), particularly when trial populations differ in effect modifiers. While methods such as Matching-Adjusted Indirect Comparison (MAIC) and Simulated Treatment Comparison (STC) exist to adjust for these cross-trial differences, software implementations have often been fragmented or limited in scope. This article introduces outstandR, an R package designed to provide a comprehensive and unified framework for population-adjusted indirect comparison (PAIC). Beyond standard weighting and regression approaches, outstandR implements advanced G-computation methods within both maximum likelihood and Bayesian frameworks, and Multiple Imputation Marginalization (MIM) to address non-collapsibility and missing data. By streamlining the workflow of covariate simulation, model standardization, and contrast estimation, outstandR enables robust and compatible evidence synthesis in complex decision-making scenarios.",
    "arxiv_id": "2601.07532",
    "authors": [
      "Nathan Green"
    ],
    "categories": [
      "stat.CO"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Population-Adjusted Indirect Treatment Comparison with the outstandR Package in R",
    "url": "https://arxiv.org/abs/2601.07532",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "c5e3d256-7bcb-4d53-a789-87a930dce6ea",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}