Associate Professor Steve Quinn
PhD, University of Tasmania, Australia; M. Math, University of Newcastle, Australia; B. Math (Hons), University of Newcastle, Australia
- Faculty of Health, Arts & Design
- School of Health Sciences
- Department of Statistics Data Science and Epidemiology
- ATC922 Hawthorn campus
A/Prof. Stephen Quinn is a senior biostatistician and currently Acting Chair of the Department of Statistics, Data Science and Epidemiology. He is experienced in all forms of multilevel mixed modelling and structural equation modelling. He is also engaged in a statistical methodological research stream in assessing goodness-of-fit in binary regression models.
A/Prof. Quinn has co-authored in over100 peer-reviewed publications and been successful in attracting funds on 19 NHMRC, ARC and partnership grants as well as many more minor grants. He uses software platforms such as Stata for conventional regression modelling and SPSS/AMOS for exploratory and confirmatory factor analysis and structural equation modelling, along with R and SAS. He is also active in consultancy in external projects.
A/Prof. Quinn’s previous academic experience includes roles with the Menzies Research Institute Tasmania as a consultant biostatistician working in the area of population health and non-communicable diseases. He has also been involved in several important Phase 3 clinical trials in biomedical research at Flinders University. He is currently the primary supervisor of one Masters Degree student (by research) and the co-supervisor of four PhD and Masters Degree students.
Mathematical modelling; Biostatistics
PhD candidate and honours supervision
Higher degrees by research
Accredited to supervise Masters & Doctoral students as Principal Supervisor.
Available to supervise honours students.
Honours topics and outlines
Evaluating logistic regression models: Goodness of fit statistics for logistic regression models have been studied extensively, but in models with only a small number or predictors. This project would extend the currently known results by considering more complex models with several continuous and dichotomous predictors, using pre-written code in various scenarios.
Goodness-of-fit for log-log models: Goodness-of-fit refers to how well the observed outcomes are modelled by the predictors in the model. Little work has been done in evaluating goodness-of-fit for most forms of binary regression. This project would examine the performance of several statistics in evaluating goodness-of-fit for log-log regression models The project involves applying already written code in various scenarios.
Fields of Research
- Statistics - 010400
- Public Health And Health Services - 111700
- 2003, Other, Dean's Honours list for an outstanding PhD, University of Tasmania
Also published as: Quinn, Steve; Quinn, S.; Quinn, Stephen; Quinn, S. J.; Quinn, Stephen J.; Quinn, Steve J.; Quinn, Steven
This publication listing is provided by Swinburne Research Bank. If you are the owner of this profile, you can update your publications using our online form.
Recent research grants awarded
- 2019: SUPER/CasesCUP Cohort *; Peter MacCallum Cancer Institute
- 2018: Australian Centre for Electromagnetic Bioeffects Research: Centres of Research Excellencein Clinical Research *; NHMRC Centres of Research Excellence
- 2018: Electroretinograms in families with autism spectrum disorder *; Alan B. Slifka Foundation
- 2018: Improving oral medication adherence and reducing medication errors in cancer treatment *; Victorian Cancer Agency Funding Scheme
- 2018: Pathways to re-incarceration among Indigenous and non-Indigenous women: The role of social, economic and psychological empowerment. *; Department of Justice, Corrective Services NSW
- 2017: Improving cardiovascular health and quality of life in people with severe mental illness: a randomised trial of a partners in health intervention *; NHMRC Project Grants
- 2017: Improving Rapid Decision-Making in the Face of Uncertainty: A randomised trial of a 1-hour troponin protocol in suspected acute coronary syndromes *; NHMRC Project Grants
* Chief Investigator
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