OR

Browse By

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 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 University of New South Wales for an year and as an Associate Research Fellow in Applied Statistics at 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 University of Liverpool in 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 prior taking the current position.

Teaching:

Dr. Darsy Darssan is appointed as a full time continuing teaching and research academic. Darsy is currently teaching postgraduate coursework subject, Introduction to Biostatistics (PUBH7630, PUBH7290).

Career Statistician:

As a career statistician Darsy is interested in developing novel statistical methodologies and publishes in Statistics Journals. The following three research streams currently flow smooth: time to event outcomes and dynamic prediction, rigid strategies for pooling study results, and design methodologies for phase I/II trials.

Service Statistician:

Darsy has long experience working as a service statistician, mainly on clinical trials, designing studies, randomisation, preparation of protocols, statistical analysis plans, final statistical reports, and participating in data safety monitoring boards. Darsy successfully provided statistical service to Biologists, Rheumatologists, Ophthalmologists, Nephrologist, Endocrinologist and Health Service Researchers. Service statistician role is currently limited to those full time academic researchers who formally recognise Darsy as an investigator in their projects at the time of preparing the research proposals.

Research Interests

  • Experimetal design methodology
    Statistical methods in designing clinical and lab based studies. Adaptive design methodologies in early phase clinical trial design. Continual reassessment method and its variations.
  • Personalized Predictive Modeling
    Dynamic prediction using statistical machine learning methods
  • Bayesian modelling
    Using statistical models under Bayesian paradigm

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

Supervision

  • Understanding the prevalence of reproductive and sexual coercion in young Australian women; a study using data from the Australian Longitudinal Study on Women¿s Health

    Doctor Philosophy

  • REthinking MOdel of Diabetes care using E-heaLth (REMODEL)

    Doctor Philosophy

View all Supervision

Available Projects

  • Integrating Dynamic and Statistical Modelling Approaches to Improve Predictions

    This PhD project will investigate the possibilities of linking Statistical modelling techniques with machine learning approaches to make realistic predictions. The project will develop systematic data-based dynamic modelling framework using supervised and un-supervised statistical machine learning techniques and integrate fractional polynomial, regression spline, and/or joint models. The student will have the opportunity to use big data collected on life course epidemiology and women’s health.

    The student must have first degree in Statistics with honours or master in Statistics or Mathematics. Experience using statistical software program R would be highly valued.

    The supervisory team will include school's lead biostatisticians.

  • Alternative Statistical Methods in Analysing Randomised Control Trials

    Generally, randomised control trials (RCTs) are analysed using a number of simple statistical techniques such as t-test, ANOVA, ANCOVA, LME models, Cox regression. This PhD project will investigate the use of advanced statistical techniques in interpreting the RCT data. This include Bayesian techniques, false discovery-based approaches, bootstrapping, and AFT models.

    In the first year the student will do a systematic review on statistical methods in RCT and possible classification of methods involved. The second year of the candidature will identify possible alternative methods and apply them on simulated data under several scenarios. The final year will be further exploration of methodologies using real data. Although this project is an applied research, quick learning of advanced statistical methodologies and exploration of computationally challenging techniques require a student with first degree in statistics.

    Experience using statistical software programs R, SAS and STATA is must. Experience in analysing RCT data will be an advantage.

    The supervisory team will include biostatisticians and clinicians.

View all Available Projects

Publications

Featured Publications

Journal Article

Conference Publication

PhD and MPhil Supervision

Current Supervision

  • Understanding the prevalence of reproductive and sexual coercion in young Australian women; a study using data from the Australian Longitudinal Study on Women¿s Health

    Doctor Philosophy — Associate Advisor

    Other advisors:

  • REthinking MOdel of Diabetes care using E-heaLth (REMODEL)

    Doctor Philosophy — Associate Advisor

    Other advisors:

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.

  • Integrating Dynamic and Statistical Modelling Approaches to Improve Predictions

    This PhD project will investigate the possibilities of linking Statistical modelling techniques with machine learning approaches to make realistic predictions. The project will develop systematic data-based dynamic modelling framework using supervised and un-supervised statistical machine learning techniques and integrate fractional polynomial, regression spline, and/or joint models. The student will have the opportunity to use big data collected on life course epidemiology and women’s health.

    The student must have first degree in Statistics with honours or master in Statistics or Mathematics. Experience using statistical software program R would be highly valued.

    The supervisory team will include school's lead biostatisticians.

  • Alternative Statistical Methods in Analysing Randomised Control Trials

    Generally, randomised control trials (RCTs) are analysed using a number of simple statistical techniques such as t-test, ANOVA, ANCOVA, LME models, Cox regression. This PhD project will investigate the use of advanced statistical techniques in interpreting the RCT data. This include Bayesian techniques, false discovery-based approaches, bootstrapping, and AFT models.

    In the first year the student will do a systematic review on statistical methods in RCT and possible classification of methods involved. The second year of the candidature will identify possible alternative methods and apply them on simulated data under several scenarios. The final year will be further exploration of methodologies using real data. Although this project is an applied research, quick learning of advanced statistical methodologies and exploration of computationally challenging techniques require a student with first degree in statistics.

    Experience using statistical software programs R, SAS and STATA is must. Experience in analysing RCT data will be an advantage.

    The supervisory team will include biostatisticians and clinicians.

For Media assistance please contact The Office of Marketing and Communications, (07) 3365 1120

Links

ResearcherID: P-5575-2017

ORCID: 0000-0001-7050-9150

Scopus ID: 56080456300

Google Scholar ID: u0VcDtIAAAAJ

Unit Links
Other Media

Related Resources

Similar Staff
Researchers in Associated Units
Fields of Research