Dr. Lifeng Lin is an Associate Professor in the Department of Epidemiology and Biostatistics at the University of Arizona, with a courtesy appointment in the Statistics and Data Science Graduate Interdisciplinary Program. His research focuses on developing and disseminating advanced statistical methodologies to enhance the validity, transparency, and robustness of evidence synthesis. His methodological expertise spans meta-analysis, network meta-analysis, Bayesian methods, multivariate modeling, fragility analysis, and the assessment of publication bias. In addition, Dr. Lin has made substantial contributions to biomedical research through collaborative applied statistical work in areas such as aging, mental health, and women’s health. His research has been supported by grants from the U.S. National Institutes of Health and the Arizona Biomedical Research Centre.

Dr. Lin has authored or co-authored over 140 peer-reviewed publications in leading journals across biostatistics, epidemiology, and clinical medicine, including the American Journal of Epidemiology, Annals of Applied Statistics, BMJ, Journal of Clinical Epidemiology, Statistics in Medicine, BMC Medicine, Epidemiology, and American Journal of Obstetrics & Gynecology. He is actively engaged in academic editorial service, serving as Associate Editor for Research Synthesis Methods and Epidemiologic Methods, Statistical Editor for Schizophrenia Bulletin and the Journal of Evidence-Based Dental Practice, and Assistant Editor for the American Journal of Obstetrics & Gynecology. He also serves as a statistical reviewer for JAMA Network Open.

As a strong advocate for open science, Dr. Lin has developed several R packages, such as altmeta, fragility, and pcnetmeta, to empower applied researchers in performing and interpreting meta-analyses with advanced statistical techniques. These tools promote reproducibility and facilitate the user-friendly application of cutting-edge methods.

Selected Publications

• Xing X, Wang Y, Lin L. Trial sequential analysis involving same-year studies requires careful temporal ordering. J Clin Epidemiol. 2025 Mar;179:111645. doi: 10.1016/j.jclinepi.2024.111645. Epub 2024 Dec 18. PMID: 39706537; PMCID: PMC11928275.
• Wang Y, DelRocco N, Lin L. Comparisons of various estimates of the I2 statistic for quantifying between-study heterogeneity in meta-analysis. Stat Methods Med Res. 2024 May;33(5):745-764. doi: 10.1177/09622802241231496. Epub 2024 Mar 19. PMID: 38502022; PMCID: PMC11759644.
• Lin L, Xing A, Chu H, Murad MH, Xu C, Baer BR, Wells MT, Sanchez-Ramos L. Assessing the robustness of results from clinical trials and meta-analyses with the fragility index. Am J Obstet Gynecol. 2023 Mar;228(3):276-282. doi: 10.1016/j.ajog.2022.08.053. Epub 2022 Sep 6. PMID: 36084702; PMCID: PMC9974556.
• Murad MH, Wang Z, Zhu Y, Saadi S, Chu H, Lin L. Methods for deriving risk difference (absolute risk reduction) from a meta-analysis. BMJ. 2023 May 5;381:e073141. doi: 10.1136/bmj-2022-073141. PMID: 37146999.
• Lin L, Aloe AM. Evaluation of various estimators for standardized mean difference in meta-analysis. Stat Med. 2021 Jan 30;40(2):403-426. doi: 10.1002/sim.8781. Epub 2020 Nov 12. PMID: 33180373; PMCID: PMC7770064.
• Lin L. Hybrid test for publication bias in meta-analysis. Stat Methods Med Res. 2020 Oct;29(10):2881-2899. doi: 10.1177/0962280220910172. Epub 2020 Apr 15. PMID: 32290810; PMCID: PMC7434640.
• Murad MH, Wang Z, Chu H, Lin L. When continuous outcomes are measured using different scales: guide for meta-analysis and interpretation. BMJ. 2019 Jan 22;364:k4817. doi: 10.1136/bmj.k4817. PMID: 30670455; PMCID: PMC6890471.
• Lin L, Chu H. Quantifying publication bias in meta-analysis. Biometrics. 2018 Sep;74(3):785-794. doi: 10.1111/biom.12817. Epub 2017 Nov 15. PMID: 29141096; PMCID: PMC5953768.
• Lin L, Zhang J, Hodges JS, Chu H. Performing arm-based network meta-analysis in R with the pcnetmeta package. J Stat Softw. 2017 Aug;80:5. doi: 10.18637/jss.v080.i05. Epub 2017 Aug 29. PMID: 28883783; PMCID: PMC5584882.
• Xu G, Lin L, Wei P, Pan W. An adaptive two-sample test for high-dimensional means. Biometrika. 2016 Sep;103(3):609-624. doi: 10.1093/biomet/asw029. Epub 2017 Mar 18. PMID: 28804142; PMCID: PMC5549874.
• Complete list in my Google Scholar profile: https://scholar.google.com/citations?user=wxnd2u8AAAAJ&hl