Biostatics workshops available through the Sydney School of Public Health in February 2026.
Clinical and Population health research often aims to determine the causal effect of a well-defined intervention. While the placebo controlled randomized trial design represents the gold standard for causal inference, such studies are often either impractical or unethical. Even if feasible, the validity of a randomized trial may be compromised by noncompliance, treatment switching, dropout, or other possible protocol violations. As a result, researchers routinely turn to observational studies where they must contend with the ubiquitous challenge of confounding bias, whereby participants in the treatment arm may not be exchangeable with those in the control arm at baseline with respect to risk factors for the outcome.
This workshop will focus on modern methods for causal identification and inference from observational data and imperfect randomized trials, focusing primarily on so-called g-methods, including propensity score methods as well as doubly robust methods assuming no unmeasured confounding. The workshop will also discuss methods to account for confounding by hidden factors, such as instrumental variable, negative controls, and proxy-based approaches. A brief survey of additional more advanced topics will include time-varying confounding, mediation analysis and interference. While the workshop focus is primarily on conceptual aspects of causal inference, numerous health applications will be discussed to illustrate the concepts and methods.
Basic knowledge of regression analysis, statistical inference and basic probability concepts.
Professor Eric Tchetgen Tchetgen is a world leader in the field of causal inference. In recognition of his ground-breaking research, he was a co-recipient of the 2022 Rousseeuw Prize awarded to Causal Inference for pioneering research on causal inference with real-world applications in medicine and public health. He is co-director of the Center for Causal Inference at Penn. He holds joint primary appointments in the Department of Statistics and Data Science at The Wharton School and in the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine. After serving on the faculty of the Harvard School of Public Health for ten years, he joined The Wharton School in 2018 as the Luddy Family President’s Distinguished Professor and Professor of Statistics and Data Science.
Physicians, clinicians, epidemiologists and public health professionals. Students of biostatistics and epidemiology, and health researchers.
Key information |
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Registration fee |
$750 (including GST) (Students: $400 (including GST)) |
Location |
The University of Sydney (Camperdown) |
Course dates |
Feb 18 - 20 2026 9:00am to 4:00pm |
Applications close |
Limited spots available |
Delivery: |
F2F and Zoom |
Computer & Software |
Not required |
Drawing on approaches from systems thinking and realist evaluation, this workshop is about mastering the art of research on social policies, programs, and phenomena within inherently complex health systems, whether in public health or global health.
Participants will learn to apply systems thinking and work out how to make sense of complexity using the context-mechanism-outcome logic of realist research. They will also learn how to optimize the presentation of their findings for impact.
Each participant will bring a research topic to the workshop, and by the end, would have developed an actionable strategy to tackle it and write about it, with opportunity for “hands-on” exercises working through the topic.
Basic knowledge of health research methods and concepts.
Associate Professor Seye Abimbola is an associate professor at the School of Public Health, University of Sydney. His teaching and research focus on epistemic practices in health systems and global health. He was the 2020-2022 Prince Claus Chair at Utrecht University in the Netherlands, the 2023-2024 Radulovacki Visiting Scholar at Northwestern University in the US, and the founding editor in chief (2015-2024) of BMJ Global Health. His book “The Foreign Gaze: Essays on Global Health” was published in 2024.He is past-Chair of the Statistical Methods for Health Economics and Outcome Research SIG – Special Interest Group of the ISPOR - International Society For Pharmacoeconomics Outcomes Research, in the Editorial Board of the PharmacoEconomics and BMC Medical Research Methodology and he is Statistical Consultant of the Lancet family journals.
Researchers and research students, focusing on public health, global health, health policy, health systems, disease control, and health equity research. Health service, public health, public policy, and health policy professionals.
Key information |
|
Registration fee |
$750 (including GST) (Students: $400 (including GST)) |
Location |
The University of Sydney (Camperdown) |
Course dates |
Feb 16 - 18 2026 9:00am to 4:00pm |
Applications close |
Limited spots available |
Delivery: |
F2F and Zoom |
Computer & Software |
You need to bring and work from your laptop. |
In health research, it is common to obtain data from the same individuals repeatedly over time. These measurements allow the direct study of changes over time, such as disease progression. In this workshop, we will present methods for analysing longitudinal data, including GEEs and mixed models for linear and non-linear effects.
The workshop will focus on the application of these methods and interpretation of the results, with plenty of opportunity for “hands-on” computational practice.
Basic knowledge of linear and logistic regression.
Professor Jaroslaw Harezlak is a Provost Professor and Chairperson of the Department of Epidemiology and Biostatistics at the Indiana University School of Public Health-Bloomington. He holds a Ph.D. in Biostatistics from Harvard University. has authored more than 160 peer-reviewed publications and a first-author book with Springer. His research spans regularization methods, functional data analysis, semiparametric regression, and collaborative studies in brain imaging, wearable computing, and network analysis.
Professor Armando Teixeira-Pinto is a Professor of Biostatistics at the School of Public Health, University of Sydney, Australia, and co-director of the Centre for Kidney Research (https://www.kidney-research.org/), a world leading research group in end-stage kidney disease, with over 50 researchers. He is also an Adjunct Professor at the University of Porto, Portugal and Adjunct Professor (honorary) at Harvard T.H. Chan School of Public Health, Executive and Continuing Professional Education.
Physicians, clinicians, epidemiologists and public health professionals. Students of biostatistics and epidemiology, and health researchers.
Key information |
|
Registration fee |
$600 (including GST) (Students: $300 (including GST)) |
Location |
The University of Sydney (Camperdown) |
Dates |
Feb 23 - Feb 24 2026 9:00am to 4:00pm |
Applications close |
Limited spots available |
Delivery: |
F2F |
Computer & Software |
You need to bring your laptop with R and RStudio installed. |
Go beyond the Cox model. As health data become increasingly complex and longitudinal, traditional survival models are often insufficient to capture the dynamic nature of real-world patient experiences. This hands-on workshop explores advanced survival techniques for analysing complex health data. We will present on time-varying covariates, parametric and flexible parametric survival models, competing risks, and multi-state modelling.
This three-day workshop is designed to equip researchers with advanced survival analysis techniques that go beyond the standard Cox model. An optional one-day pre-workshop “Introduction to Survival Analysis” will be offered for those seeking to strengthen their foundations before delving into advanced methods.
Introductory knowledge of survival analysis concepts, such as Cox proportional hazards model OR completion of the optional 1-day pre-workshop on “Introduction to survival analysis”.
Dr Nicole De La Mata is a senior lecturer in biostatistics at the University of Sydney. Her expertise lies in establishing data linkage studies and using these to apply advanced statistical models to understand patient journeys and complex clinical scenarios. Her work has broadly aimed to improve health service quality and efficiency to translate into better and more equitable patient outcomes. She has a special interest in multi-state modelling and competing risks models.
Dr Farzaneh Boroumand is a Lecturer in Biostatistics at the Sydney School of Public Health and a senior biostatistician at the Centre for Kidney Research, Westmead Hospital. She is passionate about transforming complex statistical concepts into clear, practical insights and designing interactive learning experiences that help postgraduate students apply biostatistics confidently in real-world health research. Her research focuses on survival analysis, causal inference, and chronic kidney disease, collaborating with clinicians and multidisciplinary teams to improve patient outcomes.
Associate Professor Maarit Laaksonen is an Associate Professor in Biostatistics in the Sydney School of Public Health. She leads national and international data linkage projects evaluating the determinants of the future burden of cancer with the aim of improving cancer prevention, early detection and outcomes. Maarit has a strong interest in the development and application of disease burden measures, survival models and the impact of competing risks on health outcomes.
Biostatisticians, health data researchers, physicians, clinicians, epidemiologists and public health professionals. Students of biostatistics and epidemiology, and health researchers.
Key information |
|
Registration fee |
2-day workshop $600 (including GST) (Students: $300 (including GST))
2-day workshop + 1-day pre-workshop “Introduction to survival analysis”: $750 (including GST) (Students: $400 (including GST)) |
Location |
The University of Sydney (Camperdown) |
Dates |
Pre-workshop “Introduction to survival analysis”: Feb 25 Workshop: Feb 26 - Feb 27 2026 9:00am to 4:00pm |
Applications close |
Limited spots available |
Delivery: |
F2F and Zoom |
Computer & Software |
You need to bring your laptop with R and RStudio installed. |
This course provides a comprehensive introduction to hierarchical (multilevel) modelling and Bayesian regression, two complementary approaches for analysing clustered and complex data structures common in epidemiology and public health research. Such data often arise from repeated measurements on individuals, observations nested within families or schools, or from cluster-randomized trials. Ignoring these dependencies can lead to incorrect standard errors and misleading inference.
Participants will first learn the principles of multilevel modelling, including random intercepts, random slopes, and three-level models for both continuous and binary outcomes. The course will then extend these concepts to the Bayesian framework, illustrating how prior information and hierarchical structures can be integrated to achieve flexible and coherent inference.
Teaching will combine lectures and hands-on sessions in R, providing both the theoretical foundations and practical experience needed to implement and interpret hierarchical and Bayesian regression models. By the end of this course, participants will be able to: (1) understand and apply multilevel models, (2) perform Bayesian regression in R, and (3) interpret and communicate model results effectively.
Basic knowledge of linear and logistic regression.
Professor Gian Luca Di Tanna, University of Applied Sciences and Arts of Southern Switzerland (SUPSI)
Dr. Joseph Alvin Ramos Santos, University of Applied Sciences and Arts of Southern Switzerland (SUPSI)
Physicians, clinicians, epidemiologists and public health professionals. Students of biostatistics and epidemiology, and health researchers.
Key information |
|
Registration fee |
$750 (including GST) (Students: $400 (including GST)) |
Location |
The University of Sydney (Camperdown) |
Dates |
Feb 9th to Feb 13th 8:30am to 12:30pm |
Applications close |
Limited spots available |
Delivery: |
F2F |
Computer & Software |
You need to bring your laptop with R and RStudio installed. |
Systematically synthesizing research evidence is a cornerstone of evidence-based public health, providing the foundation for informed decision-making in practice, policy, and research. A well-conducted systematic review offers a transparent and reproducible framework for identifying, appraising, and integrating findings from multiple studies. When complemented by meta-analysis, the statistical integration of quantitative evidence, researchers can obtain pooled estimates of effect sizes, explore heterogeneity, and strengthen the reliability and interpretability of the evidence base.
This course provides an introduction to the principles and methods of evidence synthesis, with an emphasis on systematic reviews and meta-analyses. Participants will learn the key steps in conducting a systematic review, including formulating research questions, developing search strategies, assessing study quality, and extracting and managing data. The course then introduces statistical techniques for meta-analysis of continuous and binary outcomes and extends these concepts with the Bayesian framework.
Teaching combines lectures and hands-on sessions, offering both the theoretical foundation and practical experience required to plan, conduct, and interpret systematic reviews and meta-analyses using both traditional and Bayesian approaches. By the end of the course, participants will be able to: (1) understand the principles of evidence synthesis; (2) perform meta-analyses using classical and Bayesian methods; and (3) critically interpret and communicate meta-analytic findings in a public health context.
No prerequisites.
Professor Gian Luca Di Tanna, University of Applied Sciences and Arts of Southern Switzerland (SUPSI)
Dr. Joseph Alvin Ramos Santos, University of Applied Sciences and Arts of Southern Switzerland (SUPSI)
Physicians, clinicians, epidemiologists and public health professionals. Students of biostatistics and epidemiology, and health researchers.
Key information |
|
Registration fee |
$750 (including GST) (Students: $400 (including GST)) |
Location |
The University of Sydney (Camperdown) |
Dates |
Feb 16 - Nov 18 2026 9:00am to 4:00pm |
Applications close |
Limited spots available |
Delivery: |
F2F |
Computer & Software |
You need to bring your laptop with R and RStudio installed. |
| Proposed Timeline | Day 1 Evidence synthesis: systematic reviews Day 2 Frequentist meta-analysis for continuous and binary outcomes Day 3 Bayesian meta-analysis |
