Leon Di Stefano
Leon Di Stefano
Dr. Leon Di Stefano is an applied statistician and Postdoctoral Research Fellow with Prof. Katherine Lee in the Clinical Epidemiology and Biostatistics Unit at the Murdoch Children's Research Institute. He conducts methodological research into the design of adaptive platform trials (APTs) and the use of ordinal outcomes and effect measures in clinical research, and is helping to design a large APT in pediatric sepsis.
Dr. Di Stefano joined the Unit in 2024 after completing his PhD at Johns Hopkins Bloomberg School of Public Health in Baltimore, USA, advised by Dr. Kasper Hansen and Prof. Elizabeth Stuart. From 2014 through 2019, Dr. Di Stefano was a computational biologist in the laboratory of Dr. Tony Papenfuss at the Walter and Eliza Hall Institute (Parkville, Australia), and prior to this he completed Bachelors and Masters degrees at the University of Melbourne.
Dr. Di Stefano joined the Unit in 2024 after completing his PhD at Johns Hopkins Bloomberg School of Public Health in Baltimore, USA, advised by Dr. Kasper Hansen and Prof. Elizabeth Stuart. From 2014 through 2019, Dr. Di Stefano was a computational biologist in the laboratory of Dr. Tony Papenfuss at the Walter and Eliza Hall Institute (Parkville, Australia), and prior to this he completed Bachelors and Masters degrees at the University of Melbourne.
Dr. Leon Di Stefano is an applied statistician and Postdoctoral Research Fellow with Prof. Katherine Lee in the Clinical Epidemiology and Biostatistics Unit at the Murdoch Children's Research Institute. He conducts methodological research into the...
Dr. Leon Di Stefano is an applied statistician and Postdoctoral Research Fellow with Prof. Katherine Lee in the Clinical Epidemiology and Biostatistics Unit at the Murdoch Children's Research Institute. He conducts methodological research into the design of adaptive platform trials (APTs) and the use of ordinal outcomes and effect measures in clinical research, and is helping to design a large APT in pediatric sepsis.
Dr. Di Stefano joined the Unit in 2024 after completing his PhD at Johns Hopkins Bloomberg School of Public Health in Baltimore, USA, advised by Dr. Kasper Hansen and Prof. Elizabeth Stuart. From 2014 through 2019, Dr. Di Stefano was a computational biologist in the laboratory of Dr. Tony Papenfuss at the Walter and Eliza Hall Institute (Parkville, Australia), and prior to this he completed Bachelors and Masters degrees at the University of Melbourne.
Dr. Di Stefano joined the Unit in 2024 after completing his PhD at Johns Hopkins Bloomberg School of Public Health in Baltimore, USA, advised by Dr. Kasper Hansen and Prof. Elizabeth Stuart. From 2014 through 2019, Dr. Di Stefano was a computational biologist in the laboratory of Dr. Tony Papenfuss at the Walter and Eliza Hall Institute (Parkville, Australia), and prior to this he completed Bachelors and Masters degrees at the University of Melbourne.
Top Publications
- Selman, CJ, Ong, SWX, Middleton, M, Di Stefano, L, Bourke, EM, Long, E, Babl, FE, Lee, KJ. Primer for Clinical Researchers on Ordinal Outcomes for Emergency Medicine.. Emerg Med Australas 38(1) : e70201 2026 view publication
- Brantner, CL, Chang, T-H, Nguyen, TQ, Hong, H, Stefano, LD, Stuart, EA. Methods for Integrating Trials and Non-experimental Data to Examine Treatment Effect Heterogeneity.. Stat Sci 38(4) : 640 -654 2023 view publication
- Di Stefano, L, Ram, M, Scharfstein, DO, Li, T, Khanal, P, Baksh, SN, McBee, N, Bengtson, CD, Gadomski, A, Geriak, M, et al. Losartan in hospitalized patients with COVID-19 in North America: An individual participant data meta-analysis.. Medicine (Baltimore) 102(23) : e33904 2023 view publication
- Brantner, CL, Chang, T-H, Nguyen, TQ, Hong, H, Di Stefano, L, Stuart, EA. Methods for Integrating Trials and Non-Experimental Data to Examine Treatment Effect Heterogeneity. 2023 view publication
- Di Stefano, L, Ogburn, EL, Ram, M, Scharfstein, DO, Li, T, Khanal, P, Baksh, SN, McBee, N, Gruber, J, Gildea, MR, et al. Hydroxychloroquine/chloroquine for the treatment of hospitalized patients with COVID-19: An individual participant data meta-analysis.. 2022 view publication
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