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Dr Darsy Darssan

Lecturer - Biostatistics

School of Public Health
Faculty of Medicine
d.darssan@uq.edu.au
+61 7 336 55272

Overview

Dr Darsy Darssan is an Accredited Professional Statistician® (PStat®) and a Fellow of Advance Higher Education (FHEA). He obtained three degrees in Statistics at mathematical sciences schools of three different universities: a Bachelor of Science with Honours in 2005 at University of Jaffna, a Master of Applied Science in 2008 at RMIT University and a Doctor of Philosophy in 2014 at Queensland University of Technology.

While doing his two years full time traditional face-to-face master degree, Darsy worked as a part-time Statistician at Australian Council for Educational Research for a year.

Between the two bouts of postgraduate studies, Darsy worked for two years: as a Statistician at the University of New South Wales for a year and another year as an Associate Research Fellow in Applied Statistics at the University of Wollongong.

While doing the highest degree in Statistics Darsy worked as a sessional academic, contributed to teaching introductory statistics to various cohorts of first-year undergraduate students. Upon completion of the doctoral degree, Darsy moved to the University of Liverpool in the UK to do his Postdoctoral research in Biostatistics. Darsy returned home in late 2015 and worked as a Biostatistician at The University of Queensland for three years before taking the current position.

Career Statistician:

As a career statistician, Darsy is interested in developing or extending statistical methodologies to solve problems that arise in real-world data analysis and data collection in Biomedical research.

Service Statistician:

Darsy has experience working as a service statistician. He mainly worked on clinical trials where he was involved in study designs, randomisation, protocols development, statistical analysis plans, final statistical reports. He actively participated in data safety monitoring boards. Darsy provided statistical service to Biologists, Rheumatologists, Ophthalmologists, Nephrologist, Endocrinologist and Health Service Researchers.

Teaching @ UQ:

Post-graduate teaching

Introduction to Biostatistics (PUBH7630)

Under-graduate teaching

Health Data Analysis (PUBH2007)

Research Interests

  • Geospatial Health Data Modeling
    Identify risk factors and quantify disease risk that explains the variation in space and time
  • Research in Biostatistics education
    Teaching and learning of Biostatistics. Methods of Biostatistics education incorporating technology tools and assessment integrated learning.
  • Modelling non-linear relationships
    Application of generalised additive models, fractional polynomials and splines. Pooling of individual participant data with non-linear patterns from different studies.
  • Predictive modeling
    Using dynamic prediction, pattern recognition, data mining, machine learning, artificial intelligence, or knowledge discovery to develop a statistical tool or models that generates a reliable prediction.

Research Impacts

I conduct research in statistical methodologies that has straightforward application in health research. My research provides steps and guidelines on advanced statistical methods for health researchers to conduct quality research.

Qualifications

  • Bachelor of Science, University of Jaffna
  • Master of Applied Science, Royal Melbourne Institute of Technology
  • Doctor of Philosophy, Queensland University of Technology

Publications

View all Publications

Grants

View all Grants

Supervision

  • The effects of maternal exposure to heatwaves on adverse pregnancy and birth outcomes in Queensland, Australia.

    Master Philosophy

  • Epidemiology of atopy and inflammation in Paediatric Cohorts.

    Doctor Philosophy

  • Dietary Patterns and Markers of Cardiometabolic Health among Young Adults in Australia: A Longitudinal Study

    Doctor Philosophy

View all Supervision

Available Projects

  • Model-based disease mapping

    In public health and epidemiology, understanding population-based human diseases on counts of observed cases at different residential areas with relevant characteristics such as age distribution, socioeconomic status, and level of environmental exposure are essential to provide better patient care and support. Such understanding involves estimating the disease risk at a small geographic area on a spatial domain of interest. Often too low observed cases at some small geographical areas will provide unreliable disease risk estimation. Unreliable estimates raise the question of whether the underlying disease risk of a geographical location is much higher or lower than the rest? This project will answer the question above through rigorous statistical modelling. The models will use the information from the neighbouring observations at various dimensions to better predict disease risk. The underlying philosophy is well known as the first low of geostatistics – “everything is related to everything else, but near things are more related”.

    For this research, based on individual research interest, the potential future PhD student has the opportunity to choose a data set from Australian children, overall health survey, women health, birth cohort, kidney disease, the study of health and aging, large-scale biomedical databases or mortality data from overseas. The above-mentioned datasets are available to conduct research, but some of those will need permission to access. Based on the individual student interest in a particular disease and data set, we will submit an expression of interest to the data custodians.

    An early career academic as a principal advisor provides many benefits to new PhD students. For example, an early career academic is readily available to listen more, care more, closely observe student progress and act quickly as necessary. The advisory team will also have a professor level co-advisor who will bring disease-specific knowledge to the research. Once established, possibly from the second year of the candidature, the student will have the opportunity to lead the project.

    Potential future PhD students will need to contact via email, attend a meeting, write a proposal, and apply to the subsequent UQGSS scholarship round.

View all Available Projects

Publications

Featured Publications

Journal Article

Conference Publication

Grants (Administered at UQ)

PhD and MPhil Supervision

Current Supervision

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Model-based disease mapping

    In public health and epidemiology, understanding population-based human diseases on counts of observed cases at different residential areas with relevant characteristics such as age distribution, socioeconomic status, and level of environmental exposure are essential to provide better patient care and support. Such understanding involves estimating the disease risk at a small geographic area on a spatial domain of interest. Often too low observed cases at some small geographical areas will provide unreliable disease risk estimation. Unreliable estimates raise the question of whether the underlying disease risk of a geographical location is much higher or lower than the rest? This project will answer the question above through rigorous statistical modelling. The models will use the information from the neighbouring observations at various dimensions to better predict disease risk. The underlying philosophy is well known as the first low of geostatistics – “everything is related to everything else, but near things are more related”.

    For this research, based on individual research interest, the potential future PhD student has the opportunity to choose a data set from Australian children, overall health survey, women health, birth cohort, kidney disease, the study of health and aging, large-scale biomedical databases or mortality data from overseas. The above-mentioned datasets are available to conduct research, but some of those will need permission to access. Based on the individual student interest in a particular disease and data set, we will submit an expression of interest to the data custodians.

    An early career academic as a principal advisor provides many benefits to new PhD students. For example, an early career academic is readily available to listen more, care more, closely observe student progress and act quickly as necessary. The advisory team will also have a professor level co-advisor who will bring disease-specific knowledge to the research. Once established, possibly from the second year of the candidature, the student will have the opportunity to lead the project.

    Potential future PhD students will need to contact via email, attend a meeting, write a proposal, and apply to the subsequent UQGSS scholarship round.

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Links

ResearcherID: P-5575-2017

ORCID: 0000-0001-7050-9150

Scopus ID: 56080456300

Google Scholar ID: u0VcDtIAAAAJ

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