Dear All Re: Biostatistics Seminars in Limerick Biostatistics Seminar Series Academic 2006/7 some details still to be confirmed . First Semester 1. H-likelihood approach to spatio-temporal modelling Prof. Youngjo Lee, Seoul National University,.South Korea. October 3rd, 3:15 Room A2002. 2. Double Hierarchical Generalized Linear Models Prof. John Nelder, Imperial College, London , UK October 24th, 3:00 Room A2002. [Postponed due to illness ] 3. Title: Modelling of mean-covariance structures in generalised estimating equations for longitudinal data Prof . Jianxin Pan , University of Manchester, UK. November 15th, 3pm Room A2002 Abstarct: When used for modelling longitudinal data generalised estimating equations specify a working structure for the within-subject covariance matrices, aiming to produce efficient parameter estimators. However, misspecification of the working covariance structure may lead to a large loss of efficiency of the estimators of the mean parameters. In this talk I will introduce an approach for joint modelling of the mean and covariance structures for longitudinal data within the framework of generalised estimating equations. The resulting estimators for the mean and covariance parameters are consistent and asymptotically Normally distributed. Real data analysis and simulation studies show that the proposed approach produces efficient estimators for both the mean and covariance parameters. 4. Analysis of Multivariate Survival Data via HGLMs Prof. Il Do Ha, Daegu Haany University, Daegu City, South Korea. November 22nd 3pm Room, A2002. Abstract: Recently, random-effect survival models such as frailty models or mixed-effect models have been widely used to analyze multivariate (or correlated) survival data in the form of recurrent or multiple-event times which often arise in the research fields of medicine or econometrics. In particular, these data can be unbalanced and/or correlated including the bivariate form, and also can be censored and/or truncated due to the study design as in classical univariate survival data. For the inference marginal likelihood methods (e.g. MCEM, GHQ), which require integration out the random effects, have been mainly developed, but becomes computationally heavier as the number of random components increases (Gueorguieva, 2001; Huber et al., 2004). This difficulty has limited the wider application of such models. Random-effect models have been recently extended to HGLMs (hierarchical generalized linear models, Lee and Nelder, 1996, 2001,2006), which allow various random structures such as crossed and/ornested structures, structured dispersion, or spatial and temporal correlations. The HGLM method based on h-likelihood (or hierarchica llikelihood) provides a statistically efficient and simple unified framework for various random-effect models. Thus, random-effect survival models can be modelled and fitted via the HGLM (Ha and Lee,2003, 2005; Ha, Lee and MacKenzie, 2006). In this talk, we introduce the various forms of multivariate survival data and then show how to model, fit and analyze such data via the HGLM. We also discuss about the practical uses and further work.. 5. Improvement of Watterson's and related estimates for the recombination rate based on shrinkage. Prof. Andreas Futschik, University of Vienna, Austria December 1st, 3pm, Room A2002 Abstract: to follow ___________________________________________________________________ Second Semester 1. Why I hate minimisation Prof. Stephen Senn , University of Glasgow, UK January 26th (Friday), 15:00 Room A2002. Minimisation is a technique of sequentially marginally balancing clinical trials. It is not based on sound design theory, brings marginal advantages compared to randomisation as regards orthogonality and some disadvantages as regards blinding. Furthermore its very debatable merits have been over-exaggerated by its proponents who tend to use the fact that they have minimised as an excuse to ignore prognostic information. In this talk I shall argue that conditioning is the way to make inferences valid in the presence of covariate information and that minimisation has no useful role in designing clinical trials. 2. Fisher information & design of quantum experiments Prof. Peter Jupp, University of St.Andrew's, Scotland,UK February 16th (Friday), 15:00, Room A2002. Quantum theory is (by its very nature) probabilistic, and so gives rise to problems in statistical inference. In classical parametric inference an important question is `What parts of the data are informative about parameters of interest?'. Key concepts here are those of Fisher information, sufficient statistic, and cut. This talk will explore some analogous concepts for quantum statistical inference. 3. Examining Spatial Heterogeneity through Geographically Weighted Regression Prof . Stewart Fotheringham, NUI . March 7th (Wednesday) ,15:00, Room A2002 Abstract: to follow. 4. Stochastic Models for Patient Care Prof. Sally McClean, University of Ulster, UK March 28th (Wednesday), 15:00, Room A2002. Abstract: to follow. 5. Hidden Markov Chain Models in Statistical Genetics Dr. David Ramsey, University of Limerick, Ireland April 18th (Wednesday), 15:00, Room A2002 Abstract: to follow. 6. TBA * Prof. Goeran Kaumerman, Bielefeld University, Germany. May 11th (Friday) 15:00 Room A2002 Abstract: to follow. 7. Modelling of molecular biological processes with genomic data Prof Ernst Wit, Lancaster University, UK May 25th (Friday) 15:00 Room A2002. The current flood of all types of genomic data raises the challenge to make our models for the underlying biological processes both relevant and feasible. We give several approaches of model-based inference of such biological processes using e.g. microarray data. *Some details to be confirmed. All welcome We take tea before the lectures around 14:45 Best Gilbert -- _____________________________ Prof. Gilbert MacKenzie Dept. of Mathematics & Statistics, University of Limerick, Limerick Ireland CoB ~ http://www.ul.ie/biostatistics ISA ~ http://www.istat.ie. Gilbert ~ http://www.staff.ul.ie/mackenzieg Email: [log in to unmask] Tel: +353 (0)61 213499 Fax: +353 (0)61 334927 _________________________