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) - since 2019

Under-graduate teaching

Health Data Analysis (PUBH2007) - since 2020

Research Interests

  • 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 accurate prediction.
  • Modelling non-linear relationships
    Pooling individual participant data from different studies using fractional polynomials and splines.
  • 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.
  • Research in Biostatistics Education
    Teaching and learning of Biostatistics. Methods of Biostatistics education incorporating technology tools and job-ready assessments.

Qualifications

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

Publications

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Supervision

View all Supervision

Available Projects

  • 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 with statistical software program R would be highly valued.

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  • 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.

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  • The ICH E9 guideline for clinical trials suggest the use of dynamic randomisation techniques to achieve balance across several factors. Multicentre trials suffer from imbalance, loss of medication packs, and operational bias when a dynamic randomisation is used. This minor research project will investigate the feasibility of implementing covariate adaptive randomisation techniques in multicentre trials.

    The project suit to a statistics student who would like to carry out an honours or master thesis in biostatistics for a year. The first part of the research will review all the variations of covariate adaptive randomisation techniques including the algorithm based minimisation, model based CAR, and distributional balance techniques. The second part of the research will estimate imbalance and logistical costs under several scenarios using simulated data. Real multicentre RCTs will be used to retrospectively estimate the logistical costs due to the choice of CAR.

    The student must have experience with R or SAS-IML or Mata-Stata.

    Reference:

    1. Hu F, Hu Y, Ma Z, Rosenberger WF. Adaptive randomization for balancing over covariates. WIREs Comput Stat. 2014; 6:288–303

    2. Taves DR. The use of minimization in clinical trials. Contemp Clin Trials. 2010 Mar; 31(2):180-4

    3. Weng H, Bateman R, Morris JC, Xiong C, Validity and power of minimization algorithm in longitudinal analysis of clinical trials. Biostat Epidemiol. 2017; 1(1): 59–77.

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View all Available Projects

Publications

Featured Publications

Journal Article

Conference Publication

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.

  • 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 with statistical software program R would be highly valued.

    -------------------------------------------------------------------------------------------

  • 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 ICH E9 guideline for clinical trials suggest the use of dynamic randomisation techniques to achieve balance across several factors. Multicentre trials suffer from imbalance, loss of medication packs, and operational bias when a dynamic randomisation is used. This minor research project will investigate the feasibility of implementing covariate adaptive randomisation techniques in multicentre trials.

    The project suit to a statistics student who would like to carry out an honours or master thesis in biostatistics for a year. The first part of the research will review all the variations of covariate adaptive randomisation techniques including the algorithm based minimisation, model based CAR, and distributional balance techniques. The second part of the research will estimate imbalance and logistical costs under several scenarios using simulated data. Real multicentre RCTs will be used to retrospectively estimate the logistical costs due to the choice of CAR.

    The student must have experience with R or SAS-IML or Mata-Stata.

    Reference:

    1. Hu F, Hu Y, Ma Z, Rosenberger WF. Adaptive randomization for balancing over covariates. WIREs Comput Stat. 2014; 6:288–303

    2. Taves DR. The use of minimization in clinical trials. Contemp Clin Trials. 2010 Mar; 31(2):180-4

    3. Weng H, Bateman R, Morris JC, Xiong C, Validity and power of minimization algorithm in longitudinal analysis of clinical trials. Biostat Epidemiol. 2017; 1(1): 59–77.

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