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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

Our sponsors

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