Zelalem Negeri, Ph.D.

I am an Assistant Professor in the Department of Statistics and Actuarial Science. I completed a two-year Postdoctoral Fellowship at McGill University before joining the University of Waterloo on July 1, 2022.

• Statistics

• Biostatistics

• Meta-analysis

• Statistical Methods

• Machine Learning

• Diagnostic Test Accuracy

• Computational Statistics

My research interests involve statistical methodology, computational statistics, and biostatistics. As a methodologist and biostatistician, I focus on advancing and validating robust statistical methods for applications in public health research with an emphasis on meta-analysis methods, the gold-standard method for evidence synthesis, using both aggregate and individual participant data. My research uses computational statistics methods, including parametric and non-parametric bootstrap approaches, as well as deterministic and Monte Carlo expectation-maximization (MCEM) algorithms.

I also apply existing statistical methods to real-life datasets to explain trends and test hypotheses. For example, through my national and international collaborations, I have contributed to and coauthored, as lead or co-lead statistician, several articles that applied existing statistical methods to solve important health research questions, such as quantifying the accuracy of depression screening tools.

• 2019, Doctorate, Mathematics(Statistics), McMaster University

• 2015, Master's, Statistics, McMaster University

• 2015, Master's, Statistics, Addis Ababa University

• 2011, Bachelor's, Statistics, Addis Ababa University

• 2024 - present Library Representative, Faculty of Mathematics, University of Waterloo

• 2023-2024 Faculty Association of the University of Waterloo (FAUW) Equity Committee

• 2022 - present Statistics and Biostatistics Seminar Series Committee, University of Waterloo

• 2024 - present Committee on New Investigators, Statistical Society of Canada

• 2018 Graduate Student Ambassadors, McMaster University

• Society for Research Synthesis Methods

• Statistical Society of Canada

• American Statistical Association

• Ethiopian Statistical Association

• STAT 230 - Probability
  • Taught in 2024, 2025
• STAT 332 - Sampling and Experimental Design
  • Taught in 2022, 2023
• STAT 947 - Topics in Biostatistics
  • Taught in 2025

* Only courses taught in the past 5 years are displayed.

• Dabi OA, Negeri ZF. Diagnostic test accuracy meta-analysis based on an exact within-study variance calculation method. Research Synthesis Methods. 2026:1-21. DOI: 10.1017/rsm.2025.10070

• Negeri ZF. A bivariate finite mixture random effects model for identifying and accommodating outliers in diagnostic test accuracy meta-analyses. Biometrical Journal. 2025;67(3):e70062. DOI: 10.1002/bimj.70062

• Zhao Y, Khan B, Negeri ZF. An evaluation of computational methods for aggregate data meta-analyses of diagnostic test accuracy studies. BMC Medical Research Methodology. 2024;24(1):111. DOI: 10.1186/s12874-024-02217-2

• Negeri ZF, Levis B, Ioannidis JP, Thombs BD, Benedetti A, the DEPRESsion Screening Data (DEPRESSD) EPDS Group. An empirical comparison of statistical methods for multiple cut-off diagnostic test accuracy meta-analysis of the Edinburgh postnatal depression scale (EPDS) depression screening tool using published results vs individual participant data. BMC Medical Research Methodology. 2024;24(1):28. DOI: 10.1186/s12874-023-02134-w

• Negeri ZF, Levis B, Sun Y, He C, Krishnan A, Wu Y, Bhandari PM, Neupane D, Brehaut E, Benedetti A, Thombs BD. Accuracy of the Patient Health Questionnaire-9 (PHQ-9) for Screening to Detect Major Depression: An Updated Systematic Review and Individual Participant Data Meta-analysis. BMJ. 2021;375. DOI: 10.1136/bmj.n2183

• Levis B, Negeri ZF, Sun Y, Benedetti A, Thombs BD, for the DEPRESsion Screening Data (DEPRESSD) EPDS Collaboration. Accuracy of the Edinburgh Postnatal Depression Scale (EPDS) for Screening to Detect Major Depression among Pregnant and Postpartum Women: Systematic Review and Meta-analysis of Individual Participant Data. BMJ 2020;371. DOI: 10.1136/bmj.m4022

• Levis B, Sun Y, He C, Wu Y, Krishnan A, Bhandari PM, Neupane D, Imran M, Brehaut E, Negeri ZF, Fischer FH. Accuracy of the PHQ-2 alone and in combination with the PHQ-9 for screening to detect major depression: systematic review and meta-analysis. JAMA. 2020;323(22):2290-300. DOI: 10.1001/jama.2020.6504

• Negeri ZF and Beyene J. Robust bivariate random-effects model for accommodating outlying and influential studies in meta-analysis of diagnostic test accuracy studies. Statistical Methods in Medical Research. 2020; 29:3308-3325. DOI: 10.1177/0962280220925840

• Negeri ZF and Beyene J. Skew-normal random-effects model for meta-analysis of diagnostic test accuracy (DTA) studies. Biometrical Journal. 2020; 62:1223–1244. DOI: 10.1002/bimj.201900184

• Negeri ZF and Beyene J. Statistical methods for detecting outlying and influential studies in meta-analysis of diagnostic test accuracy studies. Statistical Methods in Medical Research. 2020; 29:1227–1242. DOI: 10.1177/0962280219852747

• Negeri ZF, Shaikh M and Beyene J. Bivariate random-effects meta-analysis models for diagnostic test accuracy studies using arcsine-based transformations. Biometrical Journal. 2018; 60:827–844. DOI: 10.1002/bimj.201700101