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XXXIst Conference of the Austro-Swiss Region(ROeS) of the International Biometric Society
Lausanne, Switzerland, September 9-12, 2019
Scientific Program
1
Monday, September 9, 2019
09:00-12:30 Short Course Room A
SC1 Introduction to causal analysis
Martin Huber
13:30-17:00 Short Course Room A
SC2 Introduction to big data
Martin Spindler
2
Tuesday, September 10, 2019
09:00-11:10 Machine Learning and Artificial Intelligence1 / Keynote2 Room C
Opening : Valentin Rousson / Murielle Bochud
IS1 Machine learning versus traditional statistical modelling and medical doctors1
Maarten van Smeden
KS1 To infinity and beyond: lessons for big data from small experiments2
Stephen Senn
11:30-12:40 Bayesian Analysis Room C
IS2 Bayesian variable selection methodology for complex health data
Rianne Jacobs
CS1 Assessment of historical controls benefits in new clinical trials
Nicolas Sauvageot
CS2 Bayesian effect selection in structured additive distributional regression models
Helga Wagner
11:30-12:40 Precision Medicine and Biomarker Assessment Room B
CS3 An adaptive enrichment design to react on emerging biomarker data: A real case
Dominik Heinzmann
CS4 Evaluation of functional biomarkers with respect to ordinal disease severity
Amita Manatunga
IS3 Bayesian statistical learning for cancer drug screening
Manuela Zucknick
13:50-15:40 Evidence Synthesis and Meta-Analysis Room C
IS4 The march of evidence synthesis: which limits are we pushing?
Georgia Salanti
CS5 A two-stage prediction model of heterogeneous effects for many treatment options
Konstantina Chalkou
CS6 Empirical evaluation of ranking metrics in network meta-analysis
Virginia Chiocchia
CS7 A Bayesian two-step dose-response meta-analysis model
Tasnim Hamza
3
CS7 Comparing methods for variable selection in individual patient data meta-analysis
Michael Seo
13:50-15:40 Innovations in Early and Late Clinical Trials Room B
CS9 Using a non-interventional study to strengthen the evidence collected in Phase III
program: a Hemophilia A case study
Elina Asikanius
CS10 Efficient cut-point analyses
Dominik Grathwohl
CS11 Design and analysis considerations for outcome-based treatment escalation in treat-
to-target studies
Wei Wei
CS12 Comparison of time-to-first event and recurrent event methods in multiple sclerosis
trials
Marcel Wolbers
IS5 Opportunities for small data: multistate models in clinical trials
Kaspar Rufibach
16:00-17:00 Keynote speaker Room C
KS2 Statistics as a condemned building: Demolition and reconstruction
Sander Greenland
17:00-19:00 Poster session Cafe Montreux Jazz / EPFL
P01 Machine Learning and artificial intelligence in life sciences
Daniel Christen
P02 Possible association between disruptive sleep patterns in children at six months and
their later development assessed at 18 month, 3 and 5 years possibilities and challenges
when analysing large longitudinal data from a population based national registry
Milada Cvancarova Smastuen
P03 Heterogeneous Effects of Poverty on Cognition
Helmut Farbmacher
P04 Multivariate matching and propensity scores in retrospective data to conclude about
the benefit of a given medical intervention in ICU patients
Irina Irincheeva
4
P05 Survival probability estimation and group comparison for an exogenous binary time-
dependent covariate
Martina Mittlboeck
P06 Exact parametric causal mediation analysis for a binary outcome with a binary me-
diator
Martina Raggi
P07 Automated Spatio-Temporal Outbreak Detection in Low-Count Settings
Kelly Reeve
P08 Evaluation of the DMP ”Therapie aktiv - Diabetes im Griff” - results for the estab-
lished program phase
Regina Riedl
P09 A Comparison of Statistical Methods for Allocating Disease Costs in the Presence of
Interactions
Jean-Benoit Rossel
P10 Machine learning based prediction of insufficient herbage allowance with automated
grazing behaviour and activity data
Abu Zar Shafiullah
P11 Gender-related ageing trajectories in Western Europe
Valentina Shipovskaya
P12 Bring more data! - a good advice? Removing separation in logistic regression by
increasing sample size
Hana Sinkovec
P13 Methods to analyze continuous outcomes by incorporating baseline data in individual
participant data meta-analysis of non-randomized studies
Lamprini Syrogiannouli
P14 Generalizing Effect Sizes for Differences with an Alternative to Cohen’s d Coefficient
Carl Taswell
P15 Performance Evaluation of Regression Splines for Propensity Score Adjustment in
Post-Market Safety Analysis with Multiple Treatments
Yuxi Tian
P16 Machine learning based prediction of insufficient herbage allowance with automated
grazing behaviour and activity data
Stefanie von Felten
P17 Prediction of adrenal insufficiency following adrenal surgery for primary aldosteronism
by implementing discriminant analysis in complex hormone profiles before surgery
Xiao Wang
5
P18 Assessing the impact of a heterogeneity prior in Bayesian hierarchical models in terms
of added or subtracted samples to the data (effective sample size)
Manuel Wiesenfarth
P19 Model-Averaged Confidence Distribution
Jimmy Zeng
6
Wednesday, September 11, 2019
08:30-10:20 Reproducibility in Biomedical Research1 / Miscellaneous2 Room C
CS13 The H0 trial: Interpreting Fisher’s p-value as the verdict of a Neyman-Pearsonian
jury2
Michael Amiguet
CS14 Impact of a collaborating biostatistician on the quality of research: a meta-science
study protocol1
Eva Furrer
CS15 Bland & Altman quo vadis ?1
Patrick Taffe
CS16 A New Standard for the Analysis and Design of Replication Studies1
Leonhard Held
IS6 Reproducibility of animal trials: What can be done about it?1
Florian Frommlet
08:30-10:10 Machine Learning and Artificial Intelligence1 / Miscellaneous2 Room B
IS7 Uniform Inference in High-Dimensional Gaussian Graphical Models1
Martin Spindler
IS8 Machine Learning in pharma: where are we on this journey?1
Markus Lange
CS17 Statistical modelling vs. machine learning: Can flexible methods provide accurate
predictions AND interpretable effects?1
Christine Wallisch
CS18 Insights of plant electrophysiology - Using signal processing techniques and machine
learning algorithms to associate tomatoes reaction to external stimuli2
Elena Najdenovska
08:30-10:10 Multiple Testing and Adaptive Designs1 /
Causal Inference in Epidemiology2 Room A
CS19 Treatment selection in multi-arm multi-stage designs: an application to surgical trials1
Alexandra Blenkinsop
CS20 Optimized multiple testing procedures for confirmatory subgroup analysis based on a
continuous biomarker 1
Alexandra Graf
7
CS21 A closed omnibus test1
Sonia Zehetmayer
CS22 Using higher moments to test requirements for causal inference2
Wolfgang Wiedermann
CS23 Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal
Effect2
Xavier de Luna
10:50-12:20 Machine Learning and Artificial Intelligence1 / Keynote2 Room C
IS9 Plea for a marriage of machine learning and causal inference1
Els Goetghebeur
KS3 Experimenting in Equilibrium2
Stefan Waeger
8
Thursday, September 12, 2019
08:30-09:50 Young Statisticians Room C
YS1 New statistical methods to analyse evolve and resequence genetic data
Marta Pelizzola
YS2 Isotonic Regression for Growth Charts of Children
Alexandre Moesching
YS2 Rare event meta-analysis of count data: a journey across adverse settings
Romain Piaget-Rossel
YS4 A Nonparametric Approach of Interpreting the ABC-Algorithm
Victoria Racher
08:30-09:50 Precision Medicine and Biomarker Assessment1 /
Survival and Event History Analysis2 Room B
CS24 Inference in ROC surface analysis via a trinormal model-based testing approach1
Christos Nakas
CS25 Sequential models in regression of vaccine-induced antibody titers in stem cell trans-
plant recipients1
Janina Linnik
CS26 Tree-based search for predictive factors based on observational studies1
Julia Krzykalla
CS27 Comparison of Hazard Models for Distant Recurrence in Presence of Competing Risks,
namely Loco-regional Recurrence & Deaths, among Breast Cancer Patients2
Sada Nand Dwivedi
08:30-09:50 Model Selection, Prediction and Overfitting Room B
CS28 Robust and unbiased estimation framework in high-dimensionnal setting
Elise Dupuis
CS29 Quantifying degrees of necessity and of sufficiency in cause-effect relationships for
categorical and survival outcomes
Andreas Gleiss
CS30 A modified Firth correction for Poisson regression model
Ashwini Joshi
9
CS31 Comparison of likelihood penalization and variance decomposition approaches for
the derivation of binary logistic regression based low-dimensional clinical prediction
models
Anna Lohmann
10:20-11:50 Causal Inference in Epidemiology Room C
IS10 Direct and Indirect Effects based on Changes-in-Change
Martin Huber
CS32 Modified Causal Forests for Estimating Heterogeneous Causal Effects
Michael Lechner
CS33 Paradoxical findings in observational research; a new example from coronary artery
bypass surgery
Hanno Ulmer
CS34 Sensitivity analysis after propensity score matching - how strong would an unmeasured
confounder have to be to explain away the treatment effect?
Ulrike Held
10:20-11:50 Statistical Genomics1 / Causal Inference in Epidemiology2 Room B
CS35 Simultaneous Estimation of Heritability, Genetic Confounding, and Bi-directional
Causal Effect from GWAS Summary Statistics2
Liza Darrous
CS36 Leveraging correlated risks to increase power in Genome-Wide Association Studies1
Ninon Mounier
CS37 A global-local variational approach for detecting hotspots in multiple-response regres-
sion1
Helene Ruffieux
IS11 Two-sample tests on deep learning embeddings ?
Christoph Lippert
11:50-12:10 Arthur Linder Prize Ceremony Room C
AL1 Subgroup identification in clinical trials via the ”predicted individual treatment effect”
Nicolas Ballarini
AL2 Re-estimation improved two Framingham cardiovascular risk equations and the
Pooled Cohort equations: Nationwide registry analysis
Christine Wallisch
10
13:00-14:00 Miscellaneous Room C
CS38 How to interpret over/under-dispersion when modelling person-time incidence rates
with Poisson models
Rossella Belleli
CS39 Non-normality of the error term in linear regression: least squares estimator and
testing
Caroline Giacobino
CS40 Modeling extremes of flu episodes and detecting hospital congestion
Setareh Ranjbar
13:00-14:10 Model Selection, Prediction and Overfitting Room B
IS12 Searching for Truth or Profit in Data
Marcus Hudec
CS41 Transparent statistical models in the times of machine learning
Georg Heinze
CS42 New approaches for selective inference: an independent comparison
Michael Kammer
14:30-16:00 Survival and Event History Analysis Room C
CS43 Evaluating daily adherence to drug prescription from censored observations
Isabella Locatelli
CS44 Probability of random cancers as supported by the data
Janez Stare
CS45 Additive and multiplicative hazard models in practice - A series of case studies from
clinical epidemiology
Susanne Strohmaier
IS13 Sampling where the events are
Jan Beyersmann
14:30-15:50 Model Selection, Prediction and Overfitting 1 /
Bridging Biostatistics and Data Science2 Room B
CS46 Fitting linear mixed-effect models to right-skewed data in a surgical trial with an
unbalanced design1
Stefanie Hayoz
CS47 Evaluating the Cost of Simplicity of a Score to Predict a Binary Gold Standard1
Katia Iglesias
11
CS48 Possible association between disruptive sleep patterns in children at six months and
their later development assessed at 18 month, 3 and 5 years - possibilities and chal-
lenges when analysing large longitudinal data from a population based national reg-
istry2
Milada Cvancarova Smastuen
CS49 From classical statistics to machine learning and back - leveraging evidence in clinical
research2
Cheng Chen
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