{
  "abstract": "Motivation: Multivariate survival analysis with hundreds of correlated outcomes is computationally challenging. Established approaches either ignore correlations between response variables, rely on black-box deep learning or are limited to small-scale outcomes. Results: We introduce MMAD-Risk, a novel multivariate mixed accelerated failure time (AFT) model that enables scalable analysis of high-dimensional survival analysis data. We train MMAD-Risk using amortized variational inference where we design the variational distribution such that it factorizes across diseases, allowing us to decompose multivariate disease risk prediction into a series of tractable, one-dimensional problems. This allows us to calculate the ELBO analytically, enabling fast computation. The model employs a low-rank decomposition of the effect size matrix B = VW to capture shared disease mechanisms and latent random effects Vz to model comorbidity. MMAD-Risk is trained on the UK Biobank Pharma Proteomics cohort (N {approx} 55000, P {approx} 3000 proteins, D = 271 diseases). Using the full 3,000-protein dataset, MMAD-Risk achieved a mean concordance index (c-index) of 0.744 for diagnoses occurring [>=] 10 years after blood sample collection, outperforming a Cox proportional hazards model (mean c-index = 0.709). Greedy backward selection identified a 10-protein panel that preserved > 99% of the full-model performance. On this reduced panel MMAD-Risk still outperformed Cox regression (0.738 vs. 0.679).",
  "abstract_shown": true,
  "affiliations": [
    "Max Planck Institute for Multidisciplinary Sciences"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "A. M. Hilger"
    },
    {
      "affs": [
        0
      ],
      "name": "J. Soeding"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8rmjmb61d8vwh.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.16.752074",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752074v1.full",
  "id": "8rmjmb61d8vwh",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752074v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Inference"
    ]
  ],
  "openalex": "W7213541441",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752074v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8rmjmb61d8vwh.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:58Z",
      "native_id": "10.64898/2026.09.16.752074",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752074v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "MMAD-Risk: Multivariate Mixed Survival Analysis for the Prediction of Age-Dependent Disease Risks from Plasma Proteomes",
  "title_text": "MMAD-Risk: Multivariate Mixed Survival Analysis for the Prediction of Age-Dependent Disease Risks from Plasma Proteomes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:58Z",
  "url": "https://research.draconitic.com/paper/8rmjmb61d8vwh",
  "venue": "bioRxiv",
  "versions": []
}