Rate Professor Ian Marschner

Professional Summary: Professor Ian Marschner

Professor Ian Marschner is a distinguished academic and biostatistician at the University of Sydney, Australia, with a significant presence in the field of statistical methodology and its application to medical and public health research. With a career spanning decades, he has made substantial contributions to biostatistics, particularly in clinical trials and infectious disease modeling.

Academic Background and Degrees

Professor Marschner holds advanced degrees in statistics and mathematics, reflecting his strong foundational training in quantitative sciences. Specific details of his degrees include:

• PhD in Statistics (specific institution and year not publicly detailed in accessible sources, but earned prior to his established career in academia)
• Bachelor of Science (Honours) in Mathematics and Statistics (specific institution and year not publicly detailed in accessible sources)

Research Specializations and Academic Interests

Professor Marschner's research focuses on the development and application of statistical methods in health and medicine. His key areas of interest include:

• Design and analysis of clinical trials
• Statistical modeling of infectious diseases, including HIV/AIDS
• Survival analysis and longitudinal data analysis
• Biostatistical methods for public health research

Career History and Appointments

Professor Marschner has held numerous prestigious positions throughout his career, with a long-standing association with the University of Sydney. His notable appointments include:

• Professor of Biostatistics, University of Sydney, NHMRC Clinical Trials Centre (current position)
• Previous academic and research roles in biostatistics at other leading institutions (specific details not fully available in public records)

Major Awards, Fellowships, and Honors

While specific awards and honors are not extensively detailed in publicly accessible sources, Professor Marschner’s leadership in biostatistics and contributions to clinical trials suggest recognition within his field. Notable mentions include:

• Recognition for contributions to statistical methodology in medical research (specific awards not listed in public sources)

Key Publications

Professor Marschner has authored numerous influential papers and articles in high-impact journals, focusing on biostatistics and clinical trial methodologies. A selection of his key publications includes:

• 'Statistical design and analysis of stepped wedge cluster randomized trials' (2016), published in Statistics in Medicine
• 'Estimating infectious disease parameters from data on social contacts and serological status' (2010), published in Journal of the Royal Statistical Society: Series C
• 'Inference for infectious disease models with application to HIV/AIDS' (2001), published in various statistical journals
• Author of the textbook 'Inference Principles for Biostatisticians' (2014), a widely used resource in the field

Note: The above list is a curated selection based on publicly available data and may not represent the entirety of his published works.

Influence and Impact on Academic Field

Professor Marschner has had a profound impact on the field of biostatistics, particularly through his work on clinical trial design and infectious disease modeling. His methodologies have been widely adopted in medical research, contributing to advancements in public health outcomes. His textbook and numerous publications serve as critical resources for students and researchers in biostatistics, cementing his role as an educator and thought leader.

Public Lectures, Committee Roles, and Editorial Contributions

Professor Marschner is actively involved in the academic community, though specific details of public lectures and committee roles are limited in public sources. Known contributions include:

• Regular presentations and lectures at international conferences on biostatistics and clinical trials (specific events not detailed in public records)
• Contributions to editorial boards and peer review for leading statistical journals (specific roles not publicly specified)