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 / Frank Schweizer
Aggregate Networks Considered Harmful: Modeling Non-Markovian Properties of Time-Varying Network Structures 16:15-17:00 HG G 19.1
Abstract: tba

Ingo Scholtes / Frank Schweizer (ETH Zürich)

30-oct-2014 (thu)
Manuela Zucknick
tba 16:15-17:00 HG G 19.1
Abstract: tba

Manuela Zucknick (Manuela Zucknick (DKFZ), Technische Universität Dortmund)

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)

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