ZüKoSt: Seminar on Applied Statistics

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Autumn Semester 2009

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1 October 2009
16:15-17:30

Jörg Rahnenführer
Technische Universität Dortmund

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ZueKoSt: Seminar on Applied Statistics

Title	Improved interpretation of microarray data with gene groups: Cancer classification and survival prognosis
Speaker, Affiliation	Jörg Rahnenführer, Technische Universität Dortmund
Date, Time	1 October 2009, 16:15-17:30
Location	HG G 19.1
Abstract	Bioinformatics research in the post-genomic era has to cope with a ﬂood of high-dimensional data sets. The ultimate goal is a personalized medicine that uses measurements from individual patients for an improved diagnosis and therapy of diseases. The high complexity and noise levels in the data require the development and application of suitable statistical models and algorithmic procedures. However, to answer biologically relevant questions, expertise in statistics and computer science has to be combined with meaningful biological modelling. The result of a typical microarray experiment is a long list of genes with corresponding expression measurements. The interpretation of such high-dimensional data is difficult, both in terms of statistics and regarding biology and medicine. A modern, popular and promising approach for a meaningful dimension reduction is to integrate into the analysis biological a priori knowledge in the form of predefined functional gene groups, for example based on the Gene Ontology (GO). Instead of identifying important single genes, relevant groups of genes with a common biological function are detected. We present two applications for this approach, cancer classification and survival prognosis. In the first part, we describe the general procedure for scoring the statistical significance of gene groups and therefore the impact of corresponding biological processes on cancer classification. In addition, we demonstrate how this approach can be improved by integrating information on the relationships between gene groups. In the second part, we show how gene groups can be used for building survival prediction models based on the Cox regression model. We apply several feature selection procedures in order to generate predictive models for future patients. We show that adding gene groups as covariates to survival models built from single genes improves interpretability while prediction performance remains stable.

Improved interpretation of microarray data with gene groups: Cancer classification and survival prognosisread_more

HG G 19.1

22 October 2009
16:15-17:30

Paul Fearnhead
Lancaster University, UK

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ZueKoSt: Seminar on Applied Statistics

Title	Efficient Bayesian analysis of multiple changepoint models
Speaker, Affiliation	Paul Fearnhead, Lancaster University, UK
Date, Time	22 October 2009, 16:15-17:30
Location	HG G 19.1
Abstract	We describe an efficient algorithm for Bayesian analysis of multiple changepoint models. In many scenarios it enables iid samples from the posterior distribution. Approximate versions (which introduce negligble error) have a computational cost that is linear in the number of observations - and thus can be applied to large data sets (such as arise in modern bioinformatic applications). The method is demonstrated on applications that range from inference about the divergence of Salmonella Typhi and Paratyphi A, to inference about the Isochore structure of the human genome.

Efficient Bayesian analysis of multiple changepoint modelsread_more

HG G 19.1

5 November 2009
16:15-17:30

Juliane Schäfer
UniSpital Basel, Institut für klinische Epidemiologie und Biostatistik

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ZueKoSt: Seminar on Applied Statistics

Title	Predictors for change in glomerular filtration rate in HIV-infected individuals: the Swiss HIV Cohort Study
Speaker, Affiliation	Juliane Schäfer, UniSpital Basel, Institut für klinische Epidemiologie und Biostatistik
Date, Time	5 November 2009, 16:15-17:30
Location	HG G 19.1
Abstract	HIV may accelerate the loss of renal function. Evidence on the protective effect of combination antiretroviral therapy (cART) on renal function is conflicting due to the limitations of past studies to adequately model risk factors and cART components known to be related to renal function. We estimate glomerular filtration rate (GFR) with the Modification of Diet in Renal Disease (MDRD) Study equation and consider linear mixed effects models to characterize change over time and the factors that influence change, such as exposure to antiretrovirals. I will present results from this case study and share some thoughts on statistical model building.

Predictors for change in glomerular filtration rate in HIV-infected individuals: the Swiss HIV Cohort Studyread_more

HG G 19.1

12 November 2009
16:15-17:30

Jelle Goeman
Universität Leiden

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ZueKoSt: Seminar on Applied Statistics

Title	Three-sided Hypothesis Testing: Simultaneous Testing of Superiority, Equivalence and Inferiority
Speaker, Affiliation	Jelle Goeman, Universität Leiden
Date, Time	12 November 2009, 16:15-17:30
Location	HG G 19.1
Abstract	We propose three-sided testing, a testing framework for simultaneous testing of inferiority, equivalence and superiority in clinical trials, based on the partitioning principle. Like the usual two-sided testing approach, this approach is completely symmetric in the two treatments compared. Still, because the hypotheses of inferiority and superiority are tested with one-sided tests, the proposed approach has more power than the two-sided approach to infer non-inferiority. Applied to the classical point null hypothesis of equivalence, the three sided testing approach shows that it is sometimes possible to make an inference on the sign of the parameter of interest, even when the null hypothesis itself could not be rejected. Relationships with confidence intervals are explored, and the effectiveness of the three-sided testing approach is demonstrated in a number of recent clinical trails.

Three-sided Hypothesis Testing: Simultaneous Testing of Superiority, Equivalence and Inferiorityread_more

HG G 19.1

19 November 2009
16:15-17:30

Reinhard Furrer
Universität Zürich, Institut für Mathematik

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ZueKoSt: Seminar on Applied Statistics

Title	Hierarchical framework for multi-model climate projections
Speaker, Affiliation	Reinhard Furrer, Universität Zürich, Institut für Mathematik
Date, Time	19 November 2009, 16:15-17:30
Location	HG G 19.1
Abstract	While the cause of projected global climate change is largely undisputed, many details about un certainties, the inter-relation of the climate models or about regional projections still need to be addressed. We present a (Bayesian) hierarchical framework to synthesize multi-model climate projections aiming to address the aforementioned open questions. This flexible statistical technique can be applied to current or future projections, regionally or globally, and is based on the assumption that spatial patterns of climate projections can be separated into a large scale signal related to the true forced climate signal and a small scale signal stemming from model bias and internal variability. The different scales are represented via a dimension reduction technique in a hierarchical Bayes model. Posterior probabilities are obtained using a Markov chain Monte Carlo simulation technique. The method presented here takes into account uncertainty due to the use of structurally different climate models and provides PDFs of localized climate change that are nevertheless coherent with the distribution of climate change in neighboring locations.

Hierarchical framework for multi-model climate projectionsread_more

HG G 19.1

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Organisers: Andrew D. Barbour, Peter Bühlmann, Leonhard Held, Markus Kalisch, Hansruedi Künsch, Marloes Maathuis, Martin Mächler, Werner Stahel, Sara van de Geer

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