{
  "abstract": "Meta-analysis in sports science has predominantly focused on cross-sectional comparisons, leaving the temporal structure of longitudinal evidence largely unexploited. This tutorial introduces Bayesian hierarchical longitudinal meta-analysis as a principled framework for synthesizing repeated-measures data and illustrates its implementation using the R package `brms`. We demonstrate a complete analytical workflow---from model specification and prior selection to convergence checks, posterior predictive checks, and reporting—using a simulated dataset based on pubertal stature growth in male youth athletes. The worked example highlights how polynomial growth models recover biologically meaningful parameters, such as age at peak height velocity, with full uncertainty quantification. Additionally, we compare three prior specifications using leave-one-out cross-validation to test robustness. Fully annotated code scripts are provided, providing researchers with the practical tools and methodological reasoning necessary to apply longitudinal Bayesian meta-analysis to their own work in sports science and related fields.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Sandro Balbino Júnior"
    },
    {
      "name": "Ahlan Benezar Lima"
    },
    {
      "name": "Humberto M. Carvalho"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0f1xr90p2m5cv.bib",
  "cited_by": 0,
  "doi": "10.31234/osf.io/mkfsv_v1",
  "fulltext_url": null,
  "id": "0f1xr90p2m5cv",
  "issns": [],
  "landing_url": "https://osf.io/preprints/psyarxiv/mkfsv_v1/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Life Sciences"
    ],
    [
      "bepress",
      "Meta-science"
    ],
    [
      "bepress",
      "Quantitative Methods"
    ],
    [
      "bepress",
      "Social and Behavioral Sciences"
    ],
    [
      "bepress",
      "Statistical Methods"
    ],
    [
      "openalex",
      "2613|Statistical Methods and Bayesian Inference"
    ]
  ],
  "openalex": "W7204956945",
  "pdf_url": "https://osf.io/mkfsv/download",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0f1xr90p2m5cv.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:23Z",
      "native_id": "mkfsv",
      "source": "osf",
      "url": "https://osf.io/preprints/psyarxiv/mkfsv_v1/"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "bepress:Life Sciences",
      "subfield": "general"
    },
    {
      "field": "mathematics",
      "origin": "bepress:Statistical Methods",
      "subfield": "statistics"
    }
  ],
  "title": "Bayesian Hierarchical Models for Longitudinal Meta-Analysis in Sports Science: A Practical Tutorial Using brms",
  "title_text": "Bayesian Hierarchical Models for Longitudinal Meta-Analysis in Sports Science: A Practical Tutorial Using brms",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:23Z",
  "url": "https://research.draconitic.com/paper/0f1xr90p2m5cv",
  "venue": "PsyArXiv",
  "versions": []
}