Seminar for Statistics

ZüKoSt: Seminar on Applied Statistics


Time/Place: every Thursday at 4.15 pm at the Main Building of ETH, HG G 19.1

Autumn Semester 2014


Date Speaker Title Time Location
16-oct-2014 (thu)
Frank Bretz
Adaptive Methods in Clinical Trials 16:15-17:00 HG G 19.1
Abstract: Clinical trials play a critical role in pharmaceutical drug development. New trial designs often
depend on historical data, which, however, may provide inaccurate information for the current study
due to changes in study populations, patient heterogeneity, or different medical facilities. As a
result, the original plan and study design may need to be adjusted or even altered to accommodate
new findings and unexpected interim results. The goal of using adaptive methods in clinical trials
is to enhance the flexibility of trial conduct as well as maintain the integrity of trial findings.
Through carefully thought out and planned adaptation, we can pinpoint the right dose faster, treat
patients more effectively, identify treatment effects more efficiently, and thus expedite the drug
development process. In this presentation we will provide an overview of various adaptive methods
for Phase I to Phase III clinical trials. Accordingly, different types of adaptive designs will be
introduced, such as adaptive modifications of treatment randomization probabilities, adaptive dose
escalation and dose finding trials, group sequential designs (including early termination), blinded
or unblinded sample size re-estimation, and adaptive designs for treatment or subgroup selection.

Frank Bretz (Novartis, Basel)

23-oct-2014 (thu)
Ingo Scholtes und Frank Schweitzer
Modeling Time-Varying Complex Networks: The Importance of Non-Markovianity 16:15-17:00 HG G 19.1
Abstract: Recent research has highlighted limitations of studying complex systems with time-varying topologies from the perspective of static, time-aggregated networks. Non-Markovian characteristics resulting from the ordering of interactions in time-varying networks were identified as one important mechanism that alters causality and affects dynamical processes. So far, an analytical explanation for this phenomenon and for the significant variations observed across different systems is missing. Summarizing our recent research in this area, in this talk we will introduce a methodology that allows to analytically predict causality-driven changes of diffusion speed in non-Markovian time-varying networks. In particular, we will summarize the so-called "ensemble perspective" which is commonly applied in the stochastic modeling of complex networks, and we will show first results on the extension of this ensemble perspective to time-varying networks.

Ingo Scholtes und Frank Schweitzer (ETH Zürich (MTEC))

30-oct-2014 (thu)
Manuela Zucknick
Bayesian models for risk prediction with high-dimensional (integrative) genomics 16:15-17:00 HG G 19.1
Abstract: When using high-dimensional genomic data in cancer research, the identification of prognostic
factors, which can influence clinical parameters such as therapy response or survival outcome, and the evaluation of their prediction performance are some of the main issues. In these applications, the number of genome features p is usually much larger than the number of observations n (p >> n problem).
Penalized likelihood methods, for example lasso regression, are often applied in this context.
Frequentist lasso estimates correspond to Bayesian posterior mode estimates, when the regression
parameters have independent double-exponential priors. To better understand certain properties
of the lasso, it is useful to exploit this connection and to move to the Bayesian framework. I
will present a comparison study, where we investigated the lasso method in the frequentist and
Bayesian frameworks in the context of Cox models for survival endpoints.
Bayesian variable selection (BVS) can be used as an alternative way to perform risk prediction with automatic variable selection, which I will demonstrate through some applications in genomics in the second part of the talk. BVS models are very exible, both in their setup and with regards to possibilities for model inference, for example allowing to interpret and rank genomic features by their posterior variable selection probabilities. The models are exible enough to easily allow the integration of several genomic data sources in a biologically meaningful manner.

Manuela Zucknick (German Cancer Research Center (DKFZ), Heidelberg)

4-dec-2014 (thu)
Martin Schumacher
From conditional survival to dynamic predictions – aspects of application, statistical modelling and assessment 16:15-17:00 HG G 19.1
Abstract: Conditional survival (CS) is defined as the probability of surviving further t years given that a patient has already survived s years after the diagnosis of a chronic disease. It has attracted attention in recent years either in an absolute or relative form where the latter is based on a comparison with an age-adjusted normal population being highly relevant from a public health perspective. In its absolute form, CS is the quantity of major interest in a clinical context. CS constitutes the simplest form of a dynamic prediction in which other events in the course of the disease or biomarker values measured up to time s can be incorporated. In the presentation we review applications in clinical medicine, especially in oncology, and aspects related to statistical modelling with special emphasis on assessment of predictive accuracy. CS provides valuable and relevant information how prognosis develops over time; it also serves as a starting point for identifying factors related to long-term survival and for developing more complex dynamic predictions that can be used for disease monitoring.

Martin Schumacher and Stefanie Hieke
Institute of Medical Biometry and Statistics, University Medical Center Freiburg

1. Van Houwelingen HC, Putter H. Dynamic Prediction in Clinical Survival Analysis. CRC Press, Boca Raton 2012.
2. Zamboni BA et al. Conditional survival and the choice of conditioning set for patients with colon cancer. J Clin Oncol. 2010 May 20; 28 (15): 2544-8. doi: 10.1200/JCO.2009.23.0573.
3. Schoop R, Schumacher M, Graf E. Measures of prediction error for survival data with longitudinal covariates. Biom J. 2011 Mar; 53 (2): 275-93. Doi: 10.1002/bimj.201000145

Martin Schumacher (University of Freiburg)

11-dec-2014 (thu)
Syed Ejaz Ahmed
Big Data Big Bias Small Surprise! 16:15-17:00 HG G 19.1
Abstract: In high-dimensional statistics settings where number of variables is greater than observations, or when number of variables are increasing with the sample size, many penalized regularization strategies were studied for simultaneous variable selection and post-estimation. However, a model may have sparse signals as well as with number predictors with weak signals. In this scenario variable selection methods may not distinguish predictors with weak signals and sparse signals. The prediction based on a selected submodel may not be preferable in such cases. For this reason, we propose a high-dimensional shrinkage estimation strategy to improve the prediction performance of a submodel. Such a high-dimensional shrinkage estimator (HDSE) is constructed by shrinking a ridge estimator in the direction of a candidate submodel. We demonstrate that the proposed HDSE performs uniformly better than the ridge estimator. Interestingly, it improves the prediction performance of given candidate submodel generated from most existing variable selection methods. The relative performance of the proposed HDSE strategy is appraised by both simulation studies and the real data analysis.

Syed Ejaz Ahmed (Brock University, Ontario)

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