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Please NOTE, you must register here (https://protect-au.mimecast.com/s/9emIC3Q8Z2FMzzVysqXzRp?domain=statsoc.org.au) to attend this event. RSVPing this event on meetup does not mean that you registered for the workshop. You must register at the above address. ---------------------- About this workshop The Statistical Society of Australia (SSA) and the Australian Research Council (ARC) Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) are pleased to announce the following workshop: Workshop: Propensity score methods for estimating causal effects in non-experimental studies: The why, what, and how on 21 Oct 2019 at UTS in Sydney. This workshop is presented by Elizabeth A. Stuart, PhD, Professor in the Department of Mental Health at the Johns Hopkins Bloomberg School of Public Health, with joint appointments in the Department of Biostatistics and the Department of Health Policy and Management, and Associate Dean for Education at JHSPH. Professor Stuart received her PhD in statistics in 2004 from Harvard University and is a Fellow of the American Statistical Association. She has extensive experience in methods for estimating causal effects and dealing with the complications of missing data in experimental and non-experimental studies, particularly as applied to mental health, public policy, and education. This workshop is suitable for a varied audience, ranging from people with no experience with propensity scores to those with some experience who want to learn more, especially about various data complexities. General knowledge of regression and logistic regression is useful. Registrations for this event close strictly on 14 October 2019, will the Early Bird registration deadline being set for 30 September. Course venue UTS City campus, Ultimo NSW Course Fees Early Bird (payment before 1 October 2019) SSA/ACEMS Members $150 Non-Members* $495 SSA/ACEMS Student Members $100 Non-Member Students* $170 Payment after 30 September 2019 SSA/ ACEMS Members $200 Non-Members* $545 SSA/ACEMS Student Members $150 Non Member Students* $220 *Student membership is available for full-time students for $20 annually. *Full membership is available for $245 annually. For more information please click here. Registrations close on 14 October 2019. For more information about this workshop and to register, please click (https://protect-au.mimecast.com/s/9emIC3Q8Z2FMzzVysqXzRp?domain=statsoc.org.au).
Next month we have the pleasure of Prof Elizabeth Stuart all the way from John Hopkins Bloomberg School of Public Health! Date: Mon, 21st October 2019 6:00pm - 6:30pm: Refreshments 6:30pm - 7.30pm: Talk Venue: New Law School Seminar Room 344, The University of Sydney Dealing with observed and unobserved effect moderators when estimating population average treatment effects Many decisions in public health and public policy require estimation of population average treatment effects, including questions of cost effectiveness or when deciding whether to implement a screening program across a population. While randomized trials are seen as the gold standard for (internally valid) causal effects, they do not always yield accurate inferences regarding population effects. In particular, in the presence of treatment effect heterogeneity, the average treatment effect (ATE) in a randomized controlled trial (RCT) may differ from the average effect of the same treatment if applied to a target population of interest. If all treatment effect moderators are observed in the RCT and in a dataset representing the target population, then we can obtain an estimate for the target population ATE by adjusting for the difference in the distribution of the moderators between the two samples. However, that is often an unrealistic assumption in practice. This talk will discuss methods for generalizing treatment effects under that assumption, as well as sensitivity analyses for two situations: (1) where we cannot adjust for a specific moderator observed in the RCT because we do not observe it in the target population; and (2) where we are concerned that the treatment effect may be moderated by factors not observed even in the RCT. These sensitivity analyses are particularly crucial given the often limited data available from trials and on the population. The methods are applied to examples in drug abuse treatment. Implications for study design and analyses are also discussed, when interest is in a target population ATE.