Professor Richard Woodman

I have expertise in the design and analysis of randomised controlled trials and observational studies and a broad range of biostatistical techniques. My current interests include the use of supervised machine learning algorithms in Python and latent class analysis using Mplus and Stata software, and my current epidemiological focuses include nutrition and hospital mortality. I have published more than 250 papers in high ranking journals, including 150 peer reviewed articles in the past 5 years. My articles have over 8,000 citations and I have contributed to externally funded grants worth a total of AUS$15.2M including AUD$8.4M since 2016. I have provided published expert commentary on numerous topics including Bland-Altman analysis, 2-sided confidence intervals, propensity score matching, least-squares regression and pitfalls of allometric scaling. My international standing is highlighted by a consistent record of publication in leading medical journals with high impact factors including JAMA (45.5), Annals of Internal Med (21.3), Thorax (10.3), Chest (9.7), Clin Gastro Hepatology (8.5), Am J Clin Nutr (6.8), Stoke (6.0), Br J Cancer (5.7), and Sleep (5.1). Within Australia, I am a recognised expert in the reporting of indicators of patient safety including Hospital Standardised Mortality Ratios (HSMR’s) and am a member of the ACSQHC Patient Safety Reporting Committee tasked with defining KPI’s for patient safety. I am a past president of the Statistical Society of Australia (SSA) in South Australia and was a Council member for 5 years. In collaboration with the SSA I have organised methodology workshops with internationally renowned statisticians in Causal Inference, Spatial epidemiology, and Data Linkage. I have successfully supervised 10 PhD students, 2 Masters students, 3 Honours students and 5 MD students to completion, participate as a mentor in the Flinders University mentoring program and have created and taught Advanced MPH topics in Biostatistics, Epidemiology and Machine Learning.