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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 Odgen (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.
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Marie-Louise Rankin, Executive Officer
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