Ragon Institute Ghebremichael Lab
Application and development of statistical methods
Lab Overview
The Ghebremichael Lab specializes in the application and development of statistical methods, including data management, data analysis and more, for HIV/AIDS, TB, and immunologic research. We are dedicated to ensuring that studies are well-designed, correctly analyzed, clearly presented, and correctly interpreted.
We provide expertise in the planning, conduct and analysis of research with the goal of enhancing the scientific quality of research at the institute. We also provide training in software use, statistical analysis and other quantitative tools for members of the Ragon Institute. The training, given in either a course or a one-on-one meeting, includes education on statistical methods and software, such as SAS, R, and MATLAB.
Lab WebsiteHarvard CatalystMusie Ghebremichael, PhD
Affiliation
- Harvard School of Medicine, Associate Professor
About
Dr. Ghebremichael is a statistician at the Ragon Institute, an Associate Professor of Medicine at Harvard Medical School, and an Associate Investigator at the MGH Biostatistics Center. He provides statistical support to Ragon and Ragon-affiliated investigators. Dr. Ghebremichael received a bachelor’s degree in mathematics from Asmara University, Eritrea, his PhD in statistics from Rice University, and completed a post-doctoral fellowship at Yale University with a primary research focus was on statistical methods applied to HIV/AIDS research. Before joining the Ragon Institute, he worked as a statistician for Harvard University Center for AIDS Research (HU CFAR) and the Dana Farber Cancer Institute.
Related Research Foci
- Computational Science
- Artificial Intelligence and Machine Learning
- FRESH
Looking for Collaboration?
Contact UsSelected Publications
Bivariate Quantile Residual Life Functions for Estimating Immunological and Virological Recovery in HIV Patients.
Biometrical Journal, 68(2), e70112
February 28, 2026
Assessing the Performance of Longitudinal T-Lymphocytes as Biomarkers of Immune Recovery in HIV-infected Children with or without TB Co-Infection.
Journal of Applied Statistics 2025, 1–16
September 24, 2025
Comparing Estimation Methods for the Area Under the Bi-Weibull ROC Curve.
Pharmaceutical Statistics 24,no. 5 (2025): e70038
September 5, 2025
Comparison of the binormal and Lehman receiver operating characteristic curves.
Communications in Statistics-Simulation and Computation. 2022; 1-14
January 1, 2022
A comparison of machine learning techniques for classification of HIV patients with antiretroviral therapy-induced mitochondrial toxicity from those without mitochondrial toxicity
BMC Med Res Methodol. 2019 Nov 27;19(1):216
November 27, 2019
Comparing the Diagnostics Accuracy of CD4+ T-Lymphocyte Count and Percent as a Surrogate Markers of Pediatric HIV Disease
J Math Stat. 2019;15(1):55-64
January 1, 2019
The ROC Curve for Cohort Designs
Biostatistics. 2019 Jul 1;20(3):433-451
July 1, 2019
Effect of Tuberculosis on Immune Restoration among HIV-Infected Patients Receiving Antiretroviral Therapy
Journal of Applied Statistics 2018; 45(13). 2357-2364
January 1, 2018