Ph.D. Student Profile
Sabir Meah, MS
- Doctoral Student
Education
MS, Biostatistics, University of Michigan, 2023
BA, Asian Languages & Civilizations and Statistics, Amherst College, 2021
BA, Asian Languages & Civilizations and Statistics, Amherst College, 2021
Research Interests & Projects
I am primarily interested in causal inference methods development and vaccine/infectious
disease epidemiology. I am working on epidemiological and biostatistical methods
to determine and quantify the effectiveness of vaccines from observational studies.
I am interested in the development of methods that are largely agnostic to any particular
vaccine, but my applied work has been focused on respiratory illnesses, including
COVID-19.
I have also done a good deal of health services research on prostate cancer with the Michigan Urological Surgery Improvement Collaborative (MUSIC). Some of the topics of my work were the surgical overtreatment of prostate cancer and post-prostatectomy care and recurrence (including persistent positivity and biochemical recurrence). To these ends, I used time-to-event/survival and generalized linear mixed models.
I am also broadly interested in distribution-free/nonparametric methods for obtaining and (causally) analyzing observational data. I see study design as the most important step of dictating a causal analysis, ahead of the imposition of parametric assumptions. Beyond that, I am also interested in traditional nonparametric methods (e.g., rank-based tests and spline/local regression) and distribution-free machine learning methods (particularly tree-based and ensemble methods, such as superlearner) implemented novelly to make causal inference as opposed to prediction (where they are more traditionally used).
I have also done a good deal of health services research on prostate cancer with the Michigan Urological Surgery Improvement Collaborative (MUSIC). Some of the topics of my work were the surgical overtreatment of prostate cancer and post-prostatectomy care and recurrence (including persistent positivity and biochemical recurrence). To these ends, I used time-to-event/survival and generalized linear mixed models.
I am also broadly interested in distribution-free/nonparametric methods for obtaining and (causally) analyzing observational data. I see study design as the most important step of dictating a causal analysis, ahead of the imposition of parametric assumptions. Beyond that, I am also interested in traditional nonparametric methods (e.g., rank-based tests and spline/local regression) and distribution-free machine learning methods (particularly tree-based and ensemble methods, such as superlearner) implemented novelly to make causal inference as opposed to prediction (where they are more traditionally used).
Selected Publications
- Meah S, Shi X, Fritsche LG, Salvatore M, Wagner A, Martin ET, Mukherjee B. Design and analysis heterogeneity in observational studies of COVID-19 booster effectiveness: A review and case study. Sci Adv. 2023 Dec 22;9(51):eadj3747. doi: https://doi.org/10.1126/sciadv.adj3747. Epub 2023 Dec 20. PMID: 38117882; PMCID: PMC10732535.
- Monda SM, Demus T, Jaime-Casas S, Meah S, Srivastava A, Sarle R, Labardee C, Ghani KR, Ginsburg KM, Morgan TM, Borza T. Trends in Surgical Overtreatment of Prostate Cancer. JAMA Oncology. 2025 Jul 1;11(7):700-706. doi: https://doi.org/10.1001/jamaoncol.2025.0963. PMID: 40292607; PMCID: PMC12038712.
- Lewicki P, Ginsburg K, Meah S, Labardee C, Johnson A, Abdollah F, Hafron J, Semerjian A, Lane BR, Borza T; Michigan Urological Surgery Improvement Collaborative. Opioid Prescribing Patterns and Post-prostatectomy Readmission: Data From a Statewide Quality Collaborative. Urology. 2025 May 31:S0090-4295(25)00521-7. doi: https://doi.org/10.1016/j.urology.2025.05.063. Epub ahead of print. PMID: 40456453.
- Semerjian A, Fisher E, Ginsburg K, Borza T, Meah S, Labardee C, Daignault-Newton S, Boynton D, Morgan T, Triner D, Tobert CM, Lane BR; Michigan Urological Surgery Improvement Collaborative. Opportunities for Quality Improvement in Postoperative Prostate-Specific Antigen Testing After Radical Prostatectomy. Urol Pract. 2025 May 27:101097UPJ0000000000000843. doi: https://doi.org/10.1097/UPJ.0000000000000843. Epub ahead of print. PMID: 40423559.
- Fernandez Moncaleano G, Levin M, Meah S, Daignault-Newton S, Noyes SL, Sarma A, Semerjian A, Borza T, Ginsburg K, Lane BR. Uptake of Same-Day Discharge for Patients Undergoing Robot-Assisted Radical Prostatectomy in the Michigan Urological Surgery Improvement Collaborative. Urol Pract. 2024 Sep;11(5):872-882. doi: https://doi.org/10.1097/UPJ.0000000000000630. Epub 2024 Jun 24. PMID: 38913578.
Full Publication List:
https://scholar.google.com/citations?user=uB-A1kEAAAAJ&hl=en