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Challenges in the Computational Reproducibility of Linear Regression Analyses: An Empirical Study
Background: Reproducibility concerns in health research have grown, as many published results fail to be independently reproduced. Achieving computational reproducibility, where others can replicate the same results using the same methods, requires transparent reporting of statistical tests, models, and software use. While data-sharing initiatives have improved accessibility, the actual usability of shared data for reproducing research findings remains underexplored. Addressing this gap is crucial for advancing open science and ensuring that shared data meaningfully support reproducibility and enable collaboration, thereby strengthening evidence-based policy and practice. Methods: A random sample of 95 PLOS ONE health research papers from 2019 reporting linear regression was assessed for data-sharing practices and computational reproducibility. Data were accessible for 43 papers. From the randomly selected sample, the first 20 papers with available data were assessed for computational reproducibility. Three regression models per paper were reanalysed. Results: Of the 95 papers, 68 reported having data available, but 25 of these lacked the data required to reproduce the linear regression models. Only eight of 20 papers we analysed were computationally reproducible. A major barrier to reproducing the analyses was the great difficulty in matching the variables described in the paper to those in the data. Papers sometimes failed to be reproduced because the methods were not adequately described, including variable adjustments and data exclusions. Conclusion: More than half (60%) of analysed studies were not computationally reproducible, raising concerns about the credibility of the reported results and highlighting the need for greater transparency and rigour in research reporting. When data are made available, authors should provide a corresponding data dictionary with variable labels that match those used in the paper. Analysis code, model specifications, and any supporting materials detailing the steps required to reproduce the results should be deposited in a publicly accessible repository or included as supplementary files. To increase the reproducibility of statistical results, we propose a Model Location and Specification Table (MLast), which tracks where and what analyses were performed. In conjunction with a data dictionary, MLast enables the mapping of analyses, greatly aiding computational reproducibility. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement There was no cost associated with this research except for attending conferences. These costs were covered by the primary authors PhD allocation from the health faculty, Queensland University of Technology, and scholarships. The Statistical Society of Australia (SSA) and the Association for Interdisciplinary Meta-research & Open Science (AIMOS) supported the primary author with travel grants to attend their respective conferences. These scholarships did not influence the results of the study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The aim of this study was to reproduce the statistical results from publications that made their data publicly available. Authors of papers published in PLOS ONE were considered to have implicitly consented through their agreement with the journals data sharing policy, which supports the validation and reproduction of results from shared data. This study received Negligible-Low Risk Ethics approval from the Queensland University of Technology Human Research Ethics Committee (Approval Number: 2000000458). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data and a reproducible R Quarto file used to produce this paper, including tables, figures, and code, have been stored in a GitHub repository and can be cited using Zenodo dio. <https://github.com/Lee-V-Jones/Reproducibility> <https://doi.org/10.5281/zenodo.19448969>
