@article{3339648,
    title = "Heterogeneity in meta-analysis: a comprehensive overview",
    author = "Stogiannis, D. and Siannis, F. and Androulakis, E.",
    journal = "International Journal of Biostatistics",
    year = "2023",
    publisher = "De Gruyter Open Ltd",
    issn = "2194-573X, 1557-4679",
    doi = "10.1515/ijb-2022-0070",
    keywords = "human;  meta analysis;  patient coding;  practice guideline;  review;  sensitivity analysis;  software;  systematic review",
    abstract = "In recent years, meta-analysis has evolved to a critically important field of Statistics, and has significant applications in Medicine and Health Sciences. In this work we briefly present existing methodologies to conduct meta-analysis along with any discussion and recent developments accompanying them. Undoubtedly, studies brought together in a systematic review will differ in one way or another. This yields a considerable amount of variability, any kind of which may be termed heterogeneity. To this end, reports of meta-analyses commonly present a statistical test of heterogeneity when attempting to establish whether the included studies are indeed similar in terms of the reported output or not. We intend to provide an overview of the topic, discuss the potential sources of heterogeneity commonly met in the literature and provide useful guidelines on how to address this issue and to detect heterogeneity. Moreover, we review the recent developments in the Bayesian approach along with the various graphical tools and statistical software that are currently available to the analyst. In addition, we discuss sensitivity analysis issues and other approaches of understanding the causes of heterogeneity. Finally, we explore heterogeneity in meta-analysis for time to event data in a nutshell, pointing out its unique characteristics. © 2023 Walter de Gruyter GmbH, Berlin/Boston 2023."
}