UPCOMING - REGISTRATIONS OPEN NOW!
20-21 October 2022, 2-Day workshop:
Estimands, Estimators and Estimates: Aligning target of estimation, method of estimation, and sensitivity analysis
Presented by Dr Frank Bretz at the Macquarie University Sydney City Campus
Register now at: https://www.statsoc.org.au/event-4975583
Description: The ICH E9(R1) Addendum on 'Estimands and Sensitivity Analysis in Clinical Trials' introduced a framework to align planning, design, conduct, analysis, and interpretation of clinical trials. When defining the clinical question of interest, clarity is needed about 'intercurrent events' that affect either the interpretation or the existence of the measurements associated with the clinical question of interest, such as discontinuation of assigned treatment, use of an additional or alternative treatment and terminal events such as death. The description of an estimand should reflect the clinical question of interest in respect of these intercurrent events, and the Addendum introduces strategies to reflect different questions of interest that might be posed.
This two-day course will introduce the estimand framework according to the ICH E9(R1) Addendum. Using a generic example to illustrate the thinking process that aligns estimands and sensitivity analyses with trial objectives, we will provide an in-depth description of intercurrent events and various strategies for addressing them when defining the clinical question of interest. The choice of strategies can influence how more conventional attributes of a trial are reflected when describing the clinical question, for example the treatments, population or the variable (endpoint) of interest. For a given estimand, an aligned method of analysis, or estimator, should be implemented that is able to provide an estimate on which reliable interpretation can be based and which includes the handling of intercurrent events, missing data and sensitivity analyses. We will therefore also discuss how to identify and implement analyses approaches as well as sensitivity analyses that are aligned with a chosen estimand for different types of endpoints in longitudinal clinical trial settings.
About the Instructor: Dr. Frank Bretz is a Distinguished Quantitative Research Scientist at Novartis. He has supported the methodological development in various areas of pharmaceutical statistics, including dose finding, multiple comparisons, estimands, and adaptive designs. Frank is an Adjunct Professor at the Hannover Medical School (Germany) and the Medical University of Vienna (Austria). He was a member of the ICH E9(R1) Expert Working Group on 'Estimands and sensitivity analysis in clinical trials' and currently serves on the ICH E20 Expert Working Group on 'Adaptive clinical trials'. Frank is a Fellow of the American Statistical Association.
Outline: This two-day short course will include eight lectures (one hour and 30 minutes for each), with four lectures on Day 1 (October 20) and four on Day 2 (October 21).
• Introduction, motivation and scope of the ICH E9(R1) Addendum
• A framework to align planning, design, conduct, analysis and interpretation of clinical trials
• Description, strategies and construction of estimands
• Generic example to illustrate the thinking process that aligns estimands and sensitivity
• Gentle introduction to causal inference
• Intercurrent events and missing data
• Main analyses targeting estimands for different types of endpoints and strategies
• Sensitivity and supplementary analysis in light of the estimand framework
Learning Objectives: This course will focus on estimands and related statistical methodologies that are commonly used in clinical trials. We will share our experiences and try to provide some guidance on their use in clinical trial practice. The target audience includes statisticians working in industry (pharmaceutical companies), academia (universities, medical centers, or research hospitals), or government (AIHW or TGA), and also graduate students who are interested in clinical trial methods. The difficulty level of the course is intermediate, at a second-year graduate course level. The learning objectives are three-fold: (1) to understand the fundamentals of the estimand framework and be able to apply it in clinical trials; (2) to identify an appropriate primary analysis method that targets the estimand of interest, fully aligned with the ICH E9(R1) Addendum; and (3) to implement appropriate main and sensitivity analyses.
29th July 2022, 1.30 - 4.30pm:
APBG Mid Year Meeting
The George Institute, The HUB, Level 5, 1 King St, Newtown
FREE event! Please RSVP to firstname.lastname@example.org
All members and guests are invited to join us in person for refreshments and networking. The event will also be available to join online.
29th July 2022, 1.30-4.30pm
The George Institute, The HUB, Level 5, 1 King St, Newtown
1.30pm Tea, coffee and networking
2pm Dr Elisa Young – Getting to the CORE of CDISC
3pm Afternoon tea and networking
3.30pm Dr Helen Ogden – Flexible Models for Clustered Data
4.30pm Meeting Close
Getting to the CORE of CDISC
Speaker: Dr Elisa Young (Accelegan)
Abstract: The impact of CDISC standards, guidelines and initiatives on statistical programming over the last decade has been nothing short of revolutionary, stretching across clinical, non-clinical and observation science. CDISC is on a continuous journey of change, with the aim of delivering new sources of data and technology platforms. These objectives are being realised through organisational collaborations and, most importantly, a global network of volunteers. During this presentation I’ll be discussing my experience as a volunteer on the CDISC CORE initiative, including a demonstration of the conformance validator. I’ll also provide an update on other key CDISC initiatives.
Biography: Elisa is the Head of Biostatistics at Accelagen. Elisa earned a PhD in Pharmacology, followed by a Masters’ in Applied Statistics, and worked in various roles within the biotech/pharma industry, from laboratory management, bench-top science, project management and data management, before settling on a career in statistics and statistical programming. With a keen interest in CDISC, Elisa was the first Australian to obtain CDISC Tabulate Certification and is currently volunteering on the CDISC Core Project.
Flexible models for clustered data
Speaker: Dr Helen Ogden (University of Southampton)
Abstract: A wide variety of approaches are available for modelling clustered data: from assuming a single shared model for all clusters to fitting entirely separate models for each cluster. Random effects models lie between these two extremes, allowing simple variation in the model for each cluster, such as shifting a global response curve up or down by a constant (random intercept) or straight line (random slope). When the global response curve is not a straight line, these simple models sometimes fail to capture the variation in the shape of the cluster-specific response curves. I will give examples of this problem, and describe an extension to the simple random effects model which is designed to capture other types of variation.
Biography: Helen Ogden is a Lecturer in Statistics at the University of Southampton and a Fellow of the Alan Turing Institute for Data Science and Artificial Intelligence. Before that, she was a Research Fellow at the University of Warwick. Her research interests are in statistical modelling, theory and computation, with particular interest in mixed-effects models, models for count data, and conducting inference when the likelihood function is intractable.
4-5th November 2019, 2-Day workshop:
Network meta-analysis and population adjustment for decision-making
Presented by David Phillippo at the Macquarie University Sydney Campus
Day 1 of this course is aimed at statisticians, health economists, decision-makers, and systematic reviewers who are already familiar with pairwise meta-analysis, and who want to extend their knowledge to NMA and population adjustment methods. Participants will develop an understanding of these methods and the required assumptions and learn to assess and critique these types of analyses.
Day 2 is aimed at those with technical experience of meta-analysis who want to apply the knowledge learned on Day 1 to hands-on practical examples. Participants will gain experience implementing NMA and population-adjusted analyses in an R package based on the Bayesian modelling language Stan.
Network meta-analysis (NMA) is a method for combining evidence from several studies on multiple treatments of interest to provide a consistent set of relative effect estimates and is widely used for healthcare decision-making and guideline development. More recently, population adjustment methods have been proposed that use individual patient data from one or more studies to relax the assumptions of NMA and adjust for differences in effect modifying variables between populations, or even to incorporate disconnected networks and single-arm studies. The methods are becoming increasingly common in technology appraisal submissions to reimbursement agencies and raise new questions and challenges for analysts anddecision-makers.
Our workshop presenter is David Phillippo, a Senior Research Associate in Statistics at the University of Bristol, UK. His research focuses on methodology for evidence synthesis, Bayesian Network Meta-Analysis (NMA), population adjustment methods for indirect comparisons, and accounting for bias in clinical guidelines. He is the lead author of a recent Technical Support Document published by the NICE Decision Support Unit on population-adjusted indirect comparisons, and has developed new methods extending the NMA framework to incorporate population adjustment combining individual patient data and published summary data.
For more information and to register, please click https://statsoc.org.au/event-3334476
15th August:FREE Statistics Mid-Year seminar - Hurry Places are limited
When: 15 August 2019, presentations go from 1pm-4pm
Where: Eli Lilly and Company, 112 Wharf Road, West Ryde
1. Reflections of a statistician on health economic modelling in the Australian context.
Manjula Schou (Biostatistician /Research Fellow, Janssen-Cilag/Department of Mathematics and Statistics at Macquarie University, Sydney)
Manjula Schou has over 20 years of experience working as a statistician, predominantly in the pharmaceutical industry. She currently splits her time between Janssen-Cilag where she works within the Health Economics, Outcomes Research and Pricing team as a health economic modeller and company wide statistician, and the Department of Mathematics and Statistics at Macquarie University as a Research Fellow. In her later capacity Manjula's research interests are mainly focused on clinical trials methodology and statistical methods for health economic modelling.
2. The application of machine learning to personalise the care and treatment of substance use disorders.
Chrianna Bharat (Biostatistician, PhD Candidate ,National Drug & Alcohol Research Centre, Sydney)
Chrianna Bharat is a PhD Candidate at the National Drug and Alcohol Research Centre, UNSW Sydney. Chrianna’s research project focuses on the use of routinely collected data to develop predictive models to optimise treatment outcomes among people with alcohol and drug problems. After receiving her bachelor’s degree with Honours in applied statistics from the University of Western Australia, Chrianna worked as a statistical consultant for the Centre of Applied Statistics. For the past three years, Chrianna’s role as biostatistician for the Substance Use Disorder (SUD) Workgroup of the World Mental Health Survey Initiative has seen her analysing complex survey data to determine the prevalence, risk factors and treatment needs for SUDs.
Substance use disorders (SUDs) are a major contributor to the global burden of disease. Recently, the United Nations marked strengthening the prevention and treatment of SUDs as a public health priority. With machine learning methods increasingly being applied to healthcare data to support the delivery of personalised medicine, there exists the potential to use these methods to improve the processes of detecting, classifying and treating SUDs. The development of machine learning algorithms to predict SUD onset and treatment outcomes could facilitate the implementation of targeted prevention, increase SUD treatment coverage and personalise treatment allocation.
This presentation will provide a brief overview of machine learning applications in the field of alcohol and drug use disorders and describe the application of a machine learning algorithm for predicting individuals at high risk of developing an alcohol use disorder.
3. Some insights on state of the art of evidence synthesis.(Advances in meta-analysis)
Gian Luca Di Tanna (Head of Statistics Australia at the George Institute for Global Health, Sydney)
Dr Gian Luca Di Tanna is currently the Head of Statistics Australia at the George Institute for Global Health in Sydney. He has an Honours in Statistics along with a Post-graduate specialization in Medical Statistics and has obtained an MSc in Data Intelligence & Decision Making and a PhD in Health economics (Catholic University of the Sacred Heart, Italy). His Academic positions include the Sapienza University of Rome, London School of Hygiene and Tropical Medicine and the Queen Mary University of London. Dr Gian Luca Di Tanna has worked until March 2019 as a Global Health Economics Senior Manager at the Economic Modelling Centre for Excellence for Amgen, Switzerland and is affiliated with the Riskcentre - Dept. of Econometrics, Statistics and Applied Economics at the University of Barcelona, Spain. He is on the Editorial Board of the journal PharmacoEconomics, Statistical Editor of the Consumers and Communication Cochrane group and member of the Cochrane Methods Group in Systematic Reviews and Meta Analysis.
I take the opportunity of this talk to comment on the current trends in systematic reviews and highlight “established” statistical methods for meta analysis with a particular focus on some issues/assumptions that go commonly overlooked. I will illustrate the various types of meta analysis (aggregate and individual patient data, head-to-head and network meta analysis), how to deal with different study designs and discuss potential misconceptions and warnings.
Please RSVP if you plan to attend for catering purposes & entry to the building – by 26 July to myself at email@example.com
> 2015. The use of modern causal inference methods in analysing RCTs Workshop.
Presenter: Dr Richard Emsley (Centre for Biostatistics, Institute of Public Health,
University of Manchester, UK)
Location: MGSM conference centre at 37 Pitt St in the CBD in Sydney
Date: 6th December 2015
This talk will be from 4:30pm to 5:30pm, at the MGSM conference centre at 37 Pitt St in the CBD in Sydney. We will be heading to dinner afterwards with the speaker if you would like to join also (at your own expense).
Prior to the talk we will be holding our AGM (from 3:30, afternoon tea from 3pm). If you are, or would like to become an APBG member, we would love for you to attend this also. APBG members must work in the regulated healthcare area in statistics or a related field – please let us know if you would like to join.
Please RSVP to myself indicating if you will be attending the talk, the AGM or both.
For more information on the APBG (including the slides from the event you attended) please visit our website http://apbgroup.squarespace.com
> 2015. DMC Workshop.
Presenter: Dr Simon Day
Location: CBD , Sydney. TBD
Date: 26-27th February 2015
Course Description: The course is relevant to all those (statisticians and non-statisticians) who need to set up DMCs and work with them throughout the course of a clinical trial. It is also relevant to those who may serve as members of DMCs. It will focus on practical issues around the workings of data monitoring committees (DMCs) including a review of group sequential methods and FDA and CHMP guidance on DMCs. Throughout the course, mock DMC sessions will be convened where various scenarios will be considered and discussed, and decisions have to be made.
> 2014. APBG AGM
Location: Room 23, Level 4, Building 2, UTS - Room 2.4.23. 15 Broadway, Ultimo, NSW, 2007
Date: Tuesday 28 October 2014
Time: 5pm-6pm then
Talk by Professor John Carlin, Murdoch Children’s Research Institute & The University of Melbourne
Talk Topic : Assessing the risk of a rare adverse outcome following rotavirus vaccination: a case study in biostatistical methods and collaborative engagement
Location: Room 6, Level 4, Building 2, UTS Tower building- Room 2.4.6. 15 Broadway, Ultimo, NSW, 2007
Date: Tuesday 28 October 2014
Time: 6:30pm-7.30pm (Refreshments from 6pm)
> 2014 Do pharmacological interventions reduce drugs-related deaths ?
Speaker, Professor Sheila M Bird. MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK
Date: 2nd July 2014
Venue: The Sebel Hotel. 28 Albion St Sydney 2012
> 2012. Mixed Treatment Comparisons
Date: 4th 5th June 2012
Venue:MGSM Executive Hotel and Conference Centre. 99 Talavera Rd, Macquarie Park 2113 Tel. 02 9850 9082
Details: This workshop on Mixed Treatment Comparisons (MTC) is proudly supported by the Australian Pharmaceutical Biostatistics Group (APBG) and the Statistical Society of Australia (SSAI).
The workshop utilises the expertise of Professor George Wells form the Department of Epidemiology and Community Medicine Director, University of Ottawa Heart Institute.
Day 1 will be a high level overview on MTCs and their applications. There will be demonstrations of worked examples using SAS and Win BUGs. A brief outline includes: Introduction to indirect treatment comparisons Network meta-analysis methods
Part 1: Bucher approach and the Frequentist network meta-analysis - general linear mixed models Network meta-analysis methods
Part 2: Bayesian network meta-analysis - mixed treatment comparisons Worked example - Bayesian network meta-analysis Worked example -
Frequentist network meta-analysis
Day 2: will be more practical hands on session with applications of what was taught in Day 1.
There will be several breakout sessions where groups will work through examples and present back.
The day will conclude with limitations and future research.
Programs: Detailed Program Registrations: Register from SSAI Website
> 2011. Adaptive Designs for clinical trials
Date: 6-7th April 2011
Venue: MGSM Executive Hotel and Conference Centre. 99 Talavera Rd, Macquarie Park 2113 Tel. 02 9850 9082
Details: This workshop on adaptive design is proudly supported by the Australian Pharmaceutical Biostatistics Group (APBG), the Statistical Society of Australia (SSAI) and the George Institute.This workshop takes advantage of Dr Brenda Gaydos’ visit to Australia. Brenda is the Eli-Lilly specialist in adaptive designs, has a wealth of experience to share and has written several papers including one on Good Clinical Practice in this area. She will team-up with two leading researchers in adaptive designs, Dr Patrick Kelly of Sydney University, and Dr Frank Bretz of Novartis, Switzerland. Frank is an adjunct professor of the University of Hannover (Germany), and has published more than 100 papers and two books on multiplicity issues.
Programs: Detailed Program Registrations: Register from SSAI Website
> 2009. Challenges of running oncology clinical trials
Date: Thursday, 26 November 2009
Venue: Coles Theatre, School of Business, University of Melbourne, Australia
Details: This workshop is your opportunity to share your experiences and challenges of working on clinical trials in oncology. The workshop aims to take you through some of the stages during the execution of a clinical trial in oncology. It will start with discussions around developing concept outlines and protocols for oncology studies, the designs of oncology clinical trials and the subsequent logistics from a statistical, monitoring and data management perspective. From there the focus will move to safety reporting and the use of data safety monitoring boards, the differences between MedDRA and CTC coding dictionaries and why you would use one rather than the other followed by how the TGA reviews oncology studies. After lunch the discussions will become more specific around the types of measures and the corresponding analyses and the benefits and pitfalls of using the RECIST criteria. The day will finish with some ideas on how to undertake a critical appraisal of the oncology literature and a look into the future promises and challenges of oncology studies with the exciting novel therapies.
Programs: Detailed Programs Registrations: Register from SSAI Website
> 2009. ARCS 18th Annual Scientific Congress: Education into Practice
Date: 1-3 June 2009
Venue: Sydney Convention and Exhibition Centre, Australia
Details: The ARCS ASC is the premier Australian event for those involved in the development of therapeutic goods. With the theme "Education Into Practice" the 2009 ASC is focused on ensuring the transfer of knowledge into practice, with tangible take-home messages to improve professional performance.
>2008. Statistical estimates of benefits and hazards of Hormonal Therapy post-menopause: reconciling observational and trial data
Date: Wednesday, 22 October 2008
Venue: Lecture Room 2.4.11, Building 2, University of Technology Sydney, Broadway, Sydney
> 2008. Australian Statistical Conference 2008
Date: 30 June - 3 July 2008
Venue: Sofitel Melbourne, Melbourne
>2008. From randomised trials to national policy decisions via cost-effectiveness analysis
Date: 8 July 2008
Venue: Pfizer Auditorium, 38-42 Wharf Rd, West Ryde, Sydney
> 2008. Bayesian Methods in Health Economics
Date: 14 - 16 July 2008
Venue: TBC, Sydney
> 2008. Workshop on integrating statistical ideas into mathematics
Date: 6 August 2008
Venue: Macquarie Graduate School of Management Conference Centre NSW
Details: SSAI Flyer Indirect Comparison Workshop  The notes for the workshop can be downloaded here. 9.00 am
Session 1: Introduction to Indirect Comparisons - Philip McCloud 10.30 am
Session 2: Design and Interpretation Issues - Philip McCloud 11.00 am
Session 3: The use of Indirect comparisons to compliment RCT evidence – Bill Montgomery & Kristina Coleman 11.30 am
Session 4: Are all comparators Identical? - Philippa Clarke 1.00 pm
Session 5: Sources of Variation: fixed or random? - Philip McCloud 1.30 pm
Session 6: Random effects and Bayesian Methods – Patrick Fitzgerald 3.00 pm
Session 7: Double Comparators: twice the fun? - Annie Solterbeck