{
  "abstract": "Precision medicine seeks to match patients with treatments from which they are most likely to benefit. A key step toward this goal is to learn treatment-effect heterogeneity from longitudinal study data. This requires discovering subgroups with distinct treatment-effect trajectories and predicting subgroup membership for new patients from pretreatment information. Existing methods do not fully unify these tasks. Covariate-informed membership models typically prespecify the subgroup number or covariance structure, whereas data-adaptive discovery methods often require prediction in a separate stage. We propose a data-adaptive semiparametric mixture-of-experts framework that unifies longitudinal treatment-effect subgroup discovery and pretreatment membership prediction. Subgroup-specific varying-coefficient functions characterize treatment-effect trajectories, while flexible covariance functions account for within-subject dependence. A covariate-dependent gating model links membership to pretreatment characteristics. A penalty on global component weights removes redundant subgroups, while separate gating coefficients allow patient-specific membership probabilities among retained subgroups. A blockwise generalized expectation--maximization algorithm estimates all model components under a single penalized observed-data objective. We establish consistent subgroup-number selection, convergence rates for the functional estimators, and asymptotic normality for the finite-dimensional parameters. Simulations show reliable subgroup-number selection and more accurate conditional membership prediction than a two-step procedure. Applications to the SANAD epilepsy study and the COMPETE migraine trial identify distinct temporal treatment-effect patterns.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.41886",
  "author_total": 3,
  "authors": [
    {
      "name": "Pu Zhang"
    },
    {
      "name": "Yuheng Li"
    },
    {
      "name": "Jiakun Jiang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5f9ebxx2bnrbp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.41886",
  "fulltext_url": null,
  "id": "5f9ebxx2bnrbp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.41886",
  "language": null,
  "licence": "CC0-1.0",
  "licence_label": "CC0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/publicdomain/zero/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "stat.ME"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.41886",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5f9ebxx2bnrbp.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:25Z",
      "native_id": "2610.41886",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.41886"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Unifying Data-Adaptive Longitudinal Treatment-Effect Subgroup Discovery and Pretreatment Membership Prediction",
  "title_text": "Unifying Data-Adaptive Longitudinal Treatment-Effect Subgroup Discovery and Pretreatment Membership Prediction",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:53Z",
  "url": "https://research.draconitic.com/paper/5f9ebxx2bnrbp",
  "venue": "arXiv",
  "versions": []
}