Past events

THURSDAY Biostatistical Seminar: Roderic Guigò

Thursday, June 22, 2017 - 14:30

Speaker: Roderic Guigò, Professor, Centre de Regualció Genòmica, Barcelona, Spain

Title: The molecular anatomy of the human body

Note: This biostatistics seminar is jointly organised with the Sven Furberg Seminars in Bioinformatics and Statistical Genomics. At the end of the seminar simple food and refreshments will be served.

If you want to meet Dr. Roderic Guigó, please book a time slot at https://doodle.com/poll/wattnfraexmrbexy and send an email to anthony.mathelier@ncmm.uio.no with your contact information. More information below and at http://www.mn.uio.no/ifi/english/research/networks/clsi/seminars/dr.-roderic-guig%C3%B3-lecture.html

Abstract: The pilot phase of the Genotype-Tissue Expression (GTEx) project has produced RNASeq from 1,641 samples originated from up to 43 tissues from 175 post-mortem donors, and constitutes a unique resource to investigate the human transcriptome across tissues and individuals. Clustering of samples based on gene expression recapitulates tissue types, separating solid from not solid tissues, while clustering based on splicing separates neural from non-neural tissues, suggesting that post-transcriptional regulation plays a comparatively important role in the definition of neural tissues .About 47 % of the variation in gene expression can be explained by variation of across tissues, while only 4% by variation across individuals. We find that the relative contribution of individual variation is similar for lncRNAs and for protein coding genes. However, we find that genes that vary with ethnicity are enriched in lncRNAs, whereas genes that vary with age are mostly protein coding. Among genes that vary with gender, we find novel candidates both to participate and to escape X-inactivation. In addition, by merging information on GWAS we are able to identify specific candidate genes that may explain differences in susceptibility to cardiovascular diseases between males and females and different ethnic groups. We find that genes that decrease with age are involved in neurodegenerative diseases such as Parkinson and Alzheimer and identify novel candidates that could be involved in these diseases. In contrast to gene expression, splicing varies similarly among tissues and individuals, and exhibits a larger proportion of residual unexplained variance. This may reflect that that stochastic, non-functional fluctuations of the relative abundances of splice isoforms may be more common than random fluctuations of gene expression. By comparing the variation of the abundance of individual isoforms across all GTEx samples, we find that a large fraction of this variation between tissues (84%) can be simply explained by variation in bulk gene expression, with splicing variation contributing comparatively little. This strongly suggests that regulation at the primary transcription level is the main driver of tissue specificity. Although blood is the most transcriptionally distinct of the surveyed tissues, RNA levels monitored in blood may retain clinically relevant information that can be used to help assess medical or biological conditions.

Organizer: Oslo Centre for Biostatistics and Epidemiology (OCBE), Research group in Statistics and Biostatistics, Dept. of Mathematics, UiO, Big Insight and the Sven Furberg Seminars in Bioinformatics and Statistical Genomics

Location: 
Hagen 3, Forskningsparken, Oslo

Data Science: Presentasjoner av studentprosjekter og lansering av UiO: Data Science

Friday, June 16, 2017 - 18:00

Velkommen til lansering av UiO: Data Science, et forskningssamarbeid mellom institutter, sentre og forskningsgrupper på UiO.

Under lanseringen vil studenter fra STK-INF4000 gi korte presentasjoner av sine prosjekter i Data Science. I tillegg kommer Christoph Best fra Google for å snakke om Big Data Analysis.

Vi serverer gratis mat og brus, og det blir mulig å kjøpe øl og vin.

Arrangementets aldersgrense: 18 år.

Program

  • 18:00 Dørene og servering åpner. Mingling med Data Science-studenter fra UiO
  • 19:00 Foredrag og presentasjoner på scenen
  • 21:00 (ca) Opplegg på scenen ferdig, servering og mingling til lokalet stenger klokken 22

Data Science website: http://www.ub.uio.no/kurs-arrangement/arrangementer/ureal/2017/170616DataScience.html

Location: 
Realfagsbiblioteket, Vilhelm Bjerknes' hus

TUESDAY Statistics Seminar: Double seminar - Ruth Keogh and Maximilian Coblenz

Tuesday, May 30, 2017 - 14:15

We will have two talks, separated by a pause with light refreshments.

  • Speaker: Ruth Keogh (Department of Medical Statistics,  London School of Hygiene and Tropical Medicine)
  • Title: Investigating time-varying effects in Cox regression in the presence of missing data: a strategy for imputation
  • Abstract: In Cox regression analyses it is often of interest to study whether there are time-varying effects of exposures, including as a way to test the proportional hazards assumption. I will discuss the use of multiple imputation (MI) to handle missing data on exposures in this context, to avoid the loss of efficiency and potential bias that result from the ‘complete-case’ approach of dropping individuals with missing data. White and Royston (Statistics in Medicine 2009) and Bartlett et al. (SMMR 2015) described two different MI methods suitable for use with Cox regression. However, no MI methods have been devised which handle time-varying effects of exposures. I will describe extensions of the two MI methods to this setting. I will focus on time-varying effects modelled using restricted cubic splines and will outline a model building strategy which incorporates both MI and selection of time-varying effects. I will present some results from simulation studies, showing that the proposed imputation methods perform well. Failure to account for time-varying effects results in the imputation results in biased estimates and in incorrect tests for proportional hazards, even when there are in truth no time varying effects. The methods will be illustrated using data from the Rotterdam Breast Cancer Study. 

 

  • Speaker: Maximilian Coblenz (Institute of Operations Research, Karlsruhe Institute of Technology)
  • Title: Nonparametric Estimation of Multivariate Quantiles
  • Abstract: In many applications of hydrology quantiles provide important insights in the statistical problems considered. In this paper, we focus on the estimation of multivariate quantiles based on copulas. We provide a nonparametric estimation procedure for a specific notion of multivariate quantiles. These quantiles are based on particular level sets of copulas and admit the usual probabilistic interpretation that a p-quantile comprises a probability mass p. We also explore the usefulness of a smoothed bootstrap in the estimation process. Our simulation results show that the nonparametric estimation procedure yields excellent results and that the smoothed bootstrap can be beneficially applied.
Location: 
Department of Mathematics, room Sverdrups plass (lunch area) on the 8th floor of Niels Henrik Abels hus

Wednesday Lunch: Awesome Possum

Wednesday, May 24, 2017 - 12:00

Speaker: Aleksander Bai, Senior Research Scientist at Norsk Regnesentral

Title: Awesome Possum

Abstract: We will present the Awesome Possum project where the goal is to get rid of passwords. This is an innovation project involving Telenor, Signicat, NR, NTNU, and UPM. We want to use sensors in your mobile phone to identify and authenticate you, so you never have to type another password again. We will talk about which sensors we use, how we are gathering data and what we are doing with it. We will also discuss some of the issues we have by trying to fit models into the phone itself.

The lunch starts at 12:00, and the talk will start around 12:20.

NB: Wednesday BigInsight Lunches are open to staff and students from any of the BigInsight partners, including UiO, but not to others

Location: 
Sverdrups plass (lunch area), 8th floor N. H. Abels hus, Department of Mathematics

TUESDAY Statistics Seminar: Willi Sauerbrei

Tuesday, May 9, 2017 - 14:15

Speaker: Willi Sauerbrei (Institute for Medical Biometry and Statistics, University of Freiburg)

Title: Regression model-building with continuous variables – multivariable fractional polynomials, with extensions for interactions

Abstract: In the analysis of studies in clinical epidemiology, the number of candidate variables for a regression model is often too large and a more parsimonious model is sought. Another key issue is the determination of appropriate dose-response functions for continuous covariates. Often, continuous predictors are either categorized or linearity is assumed. However, both approaches can have major disadvantages and models incorporating non-linear functions may markedly improve the fit. The method of multivariable fractional polynomials (MFP) simultaneously determines suitable functional forms for continuous covariates and eliminates uninfluential covariates (1,2,3). The method also allows categorical and binary covariates.

By analysing data in the framework of linear, logistic and Cox regression models, we discuss model-building issues with an emphasis on MFP. Extensions of MFP have been developed to investigate for interactions between continuous covariates and treatment (MFPI), between two continuous covariates (MFPIgen) and for interactions with time (non-proportional hazards, MFPT) in a Cox model (3,4,5). Using data from a large cohort study, we show that mis-modelling non-linear main effects can introduce spurious interactions between two continuous covariates. In RCTs, we illustrate that our approach has power to identify differential treatment effects, and demonstrate how to estimate and plot a continuous treatment-effect function. In a large simulation studies we could show that MFPI has advantages to several alternative approaches (5).

We conclude that MFP and its extensions for interactions are useful in multivariable model-building with continuous and categorical variables. MFP software for Stata, SAS and R is generally available (6).

Joint work with Patrick Royston (MRC Clinical Trials Unit, London, UK). For more details see http://mfp.imbi.uni-freiburg.de/

References:

  1. Royston P and Altman DG (1994): Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling (with disc.) Applied Statistics, 43: 429-467
  2. Sauerbrei W and Royston P (1999): Building multivariable prognostic and diagnostic models: transformation of the predictors using fractional polynomials. Journal of the Royal Statistical Society, Series A, 162: 71-94
  3. Royston P, Sauerbrei, W (2008): ‘Multivariable Model-Building – A pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables’. Wiley.
  4. Sauerbrei W, Royston P, Look M (2007): A new proposal for multivariable modelling of time-varying effects in survival data based on fractional polynomial time-transformation. Biometrical Journal, 49: 453-473
  5. Royston P., Sauerbrei W. (2014): Interaction of treatment with a continuous variable: simulation study of power for several methods of analysis. Statistics in Medicine, 33: 4695-4708
  6. Sauerbrei W, Meier-Hirmer C, Benner A, Royston P (2006): Multivariable regression model building by using fractional polynomials: description of SAS, STATA and R programs, Computational Statistics and Data Analysis, 50: 3464-3485
Location: 
Department of Mathematics, room Sverdrups plass (lunch area) on the 8th floor of Niels Henrik Abels hus

THURSDAY Biostatistical Seminar: Jukka Corander

Thursday, May 4, 2017 - 14:15

Speaker: Jukka Corander, Professor, Oslo Centre for Biostatistics and Epidemiology, Dept. of Biostatistics, University of Oslo

Title: Fast inference for intractable ultra high-dimensional Potts models for genome sequence data

Coffee/tea served from 14:15 and the talk will start around 14:30.

Abstract: The potential for genome-wide modeling of epistasis has recently surfaced given the possibility of sequencing densely sampled populations and the emerging families of statistical interaction models. Direct coupling analysis (DCA) has earlier been shown to yield valuable predictions for single protein structures, and has recently been extended to genome-wide analysis of bacteria, identifying novel interactions in the co-evolution between resistance, virulence and core genome elements. However, earlier computational DCA methods have not been scalable to enable model fitting simultaneously to 104-105 polymorphisms, representing the upper bound of variation observed in genomic analyses of many bacterial species. We will introduce a novel inference method (SuperDCA) which employs a new scoring principle, efficient parallelization, optimization and filtering on phylogenetic information to achieve scalability for up to 105 polymorphisms. Using large population samples of Streptococcus pneumoniae, we demonstrate the ability of SuperDCA to make significant biological findings about this major human pathogen. We also show that our method can uncover weak signals of selection that are not detectable by genome-wide association analysis, even though our analysis does not require phenotypic measurements. SuperDCA thus holds considerable potential in building understanding about numerous organisms at a systems biological level.

Organizer: Oslo Centre for Biostatistics and Epidemiology (OCBE), Research group in Statistics and Biostatistics, Dept. of Mathematics, UiO and Big Insight

Location: 
Domus Medica, room 2240 (Dep. of Biochemistry)

Wednesday Lunch:

Wednesday, May 3, 2017 - 12:00

Speakers: Qinghua Liu (Sylvia) (PhD candidate, Dept of Mathematics), Vinnie Ko (PhD candidate, Dept of Mathematics), Solveig Engebretsen (PhD candidate, Dept of Biostatistics), Vera Djordjilovic (Postdoc, Dept of Biostatistics), Andrew Henry Reiner (Researcher, Dept of Biostatistics) and Andrea Cremaschi (Postdoc, Centre for Molecular Medicine)

We will have a presentation of some of the PhD candiates and resesarchers that are connected to BigInsight. They will all give a short presentation of themeselves and the project they are working on.
 
 

The lunch starts at 12:00, and the talk will start around 12:20.

 

NB: Wednesday BigInsight Lunches are open to staff and students from any of the BigInsight partners, including UiO, but not to others

Location: 
Spiseriet, Norwegian Computing Center

THURSDAY Biostatistical Seminar: Robin Andersson

Thursday, April 27, 2017 - 14:30

Speaker: Robin Andersson, Assistant Professor, The Bioinformatics Centre, University of Copenhagen, Denmark

Title: Characterisation of regulatory activities and active chromatin architectures from transcription initiation events

Note: This biostatistics seminar is jointly organised with the Sven Furberg Seminars in Bioinformatics and Statistical Genomics. At the end of the seminar simple food and refreshments will be served.

Abstract: The correct activities of gene regulatory elements and their interplay are essential for the correct coordinated transcriptional activities within a cell. Transcription is regulated in part by events at gene promoters and at gene-distal transcriptional enhancers, whose activities are influenced by local chromatin characteristics and favourable chromatin architectures bringing distant enhancers close to their target promoters in three-dimensional space.

In this talk, I will describe our efforts to characterise the inherent transcriptional activities at regulatory elements using Cap Analysis of Gene Expression (CAGE) and how such data can be used as a proxy to infer the regulatory activities of a cell. I will further present our work on modelling regulatory architectures by decomposition of expression data into positionally dependent and independent components. The independent component carries information about promoter-localised and expression-level associated effects. The positional component is highly reflective of chromatin organisation, revealing chromatin compartments, boundaries of transcriptionally active topologically associating domains, and proximity interactions as defined by chromatin conformation capture data. In all, our work demonstrates a close relationship between transcription and higher order regulatory organisations.

Organizer: Oslo Centre for Biostatistics and Epidemiology (OCBE), Research group in Statistics and Biostatistics, Dept. of Mathematics, UiO, Big Insight and the Sven Furberg Seminars in Bioinformatics and Statistical Genomics

Location: 
Forskningsparken (Oslo Science Park), HAGEN 3

TUESDAY Statistics Seminar: Aliaksandr Hubin

Tuesday, April 25, 2017 - 14:15

Speaker: Aliaksandr Hubin (Department of Mathematics, University of Oslo) 

Title: A novel algorithmic approach to Bayesian Logic Regression

Abstract: Logic regression was developed more than a decade ago as a tool to construct predictors from Boolean combinations of binary covariates. It has been mainly used to model epistatic effects in genetic association studies, which is very appealing due to the intuitive interpretation of logic expressions to describe the interaction between genetic variations. We introduce an advanced evolutionary algorithm called GMJMCMC (Genetically modified Mode Jumping Markov Chain Monte Carlo) to perform Bayesian model selection in the space of logic regression models. After describing the algorithmic details of GMJMCMC we perform a comprehensive simulation study that illustrates its high performance for logic terms of various complexity. Specifically GMJMCMC is shown to be able to identify three-way and even four-way interactions with large power, whilst keeping FDR reasonably small. This level of complexity has not been achieved by previous implementations of Bayesian logic regression. Finally, we apply GMJMCMC to reanalyze QTL mapping data for Recombinant Inbred Lines in Arabidopsis thaliana and from a backcross population in Drosophila where we identify several interesting epistatic effects.

Location: 
Department of Mathematics, room Sverdrups plass (lunch area) on the 8th floor of Niels Henrik Abels hus

THURSDAY Biostatistical Seminar: Georg Heinze

Thursday, March 30, 2017 - 14:15

Speaker: Georg Heinze, Professor, Center for Medical Statistics, Informatics, and Intelligent Systems (Section for Clinical Biometrics), Medical University of Vienna, Austria

Title: The multiple faces of shrinkage

Coffee/tea served from 14:15 and the talk will start around 14:30.

Abstract: Shrinkage is a result of overfitting, if regression models are estimated with small or sparse data sets.  In such situations predictions for new subjects are often ‘too extreme’ and their real outcomes are closer to an overall mean, i.e. they appear to be ‘shrunken’. Interestingly, ‘shrinkage’ is also often used to denote estimators that aim at anticipating shrinkage effects and preventing its occurrence. This duality has often caused confusion.

Shrinkage estimators can serve various purposes. Some methods were developed to optimize the calibration of prediction models. Other methods should reduce bias away from zero, which in logistic regression problems with small samples can be severe, but is absent, e.g., in linear regression. Another purpose could be to improve the accuracy, i.e., to reduce mean squared error of predictions or of effect estimates. Irrespective of their purpose, some of these methods can have a Bayesian motivation, where prior belief about possible values of common estimands such as log odds ratios is expressed as prior distributions centered at zero.

Shrinkage estimators can be constructed by maximizing a likelihood function penalized by an additional function of the parameters, which pulls estimates towards zero. Ridge and lasso regression are well-known examples, and so is Firth’s penalized likelihood. Other shrinkage estimators are constructed differently, e.g., estimating and applying post-estimation shrinkage factors by resampling methods. It is less well known that also classical variable selection methods can be interpreted as shrinkage estimators.

The talk will mainly focus on the setting of logistic regression with rare events. After a general introduction, we will compare shrinkage estimators by their assumed ‘pessimism’, i.e., the amount of overfitting that they anticipate (Kammer et al, 2017). Subsequently, we will investigate the improvement in accuracy of parameter estimates and predicted probabilities implicated with various shrinkage methods (Puhr et al, 2017). Finally, we will briefly discuss Bayesian noncollapsibility, i.e., likelihood penalization resulting in undesired anti-shrinkage, which can affect all well-known shrinkage estimators (Greenland, 2010; Geroldinger et al, 2017).

Organizer: Oslo Centre for Biostatistics and Epidemiology (OCBE), Research group in Statistics and Biostatistics, Dept. of Mathematics, UiO and Big Insight

Location: 
Domus Medica, room 2240 (Dep. of Biochemistry)

Wednesday Lunch: Steffen Sjursen

Wednesday, March 29, 2017 - 12:00

Speaker: Steffen Sjursen, Senior analyst, group risk modelling, DNB. PhD UiO: Stochastic Optimal Control and time changed Lévy noises.

Title: Calculating probability of default when available data varies greatly between different customers.

Abstract: TBA

The lunch starts at 12:00, and the talk will start around 12:20.

NB: Wednesday BigInsight Lunches are open to staff and students from any of the BigInsight partners, including UiO, but not to others

 

Location: 
Spiseriet, Norwegian Computing Center

TUESDAY Statistics Seminar: Nicola Lunardon

Tuesday, March 28, 2017 - 14:15

Speaker: Nicola Lunardon, University of Milano-Bicocca

Title: On bias prevention and incidental parameters.

Abstract: Firth (1993, Biometrika) introduced the bias prevention approach with the aim to reduce the bias of the maximum likelihood estimator. It is shown that the methodology is also effective in reducing the sensitivity of the derived inferential procedures in models involving incidental parameters.

Location: 
Department of Mathematics, room Sverdrups plass (lunch area) on the 8th floor of Niels Henrik Abels hus

THURSDAY Biostatistical Seminar: Harald Binder

Thursday, March 16, 2017 - 14:15

Speaker: Harald Binder, Professor, Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center, Johannes Gutenberg University Mainz, Germany.

Title: Two uses of stagewise regression: from landmarking in cancer patients to deep learning for SNPs

Coffee/tea served from 14:15 and the talk will start around 14:30.

Abstract: Regularized techniques allow to estimate the parameters of a regression model and to perform variable selection even when the number of covariates is larger than the number of observations.  Penalized likelihood approaches are the most prominent way for performing such regularization, e.g. the Lasso for variable selection. Stagewise regression techniques, also known as componentwise boosting, provide an alternative to the latter. Starting from estimates all equal to zero, one parameter is updated in each of a potentially large number of steps. Typically, this does not stop at the maximum of a global criterion, such as a penalized log-likelihood, which makes such approaches difficult to treat analytically might be seen as a disadvantage. Yet, there are two distinct advantages: optimization in settings where no (overall) likelihood is a available, and speed. I will show two applications, where these advantages play out. In a dynamic prediction application with data from hepatocellular carcinoma patients, effects of patient characteristics on the cumulative incidence of death (in presence of competing risks) are investigated using pseudo-value regression models at different landmarks. Stagewise regression allows to couple variable selection between landmarks without enforcing similarity between parameter estimates. As a result the approach selects a set of variables that is relevant for all landmarks. In a second application, the aim is to search for potentially complex patterns in molecular data, namely single nucleotide polymorphisms (SNPs), which might be relevant for patient prognosis. The modeling of patterns is performed by a deep learning approach, specifically deep Boltzmann machines, which cannot be applied in a straightforward manner if the number of SNPs is larger than the number of observations. Thus, stagewise regression is used to obtain a rather crude (implicit) estimate of the joint distribution of SNPs, which forms the basis for partitioned training of deep Boltzmann machines. The resulting SNP patterns are seen to be relevant with respect to clinical outcomes.

Organizer: Oslo Centre for Biostatistics and Epidemiology (OCBE), Research group in Statistics and Biostatistics, Dept. of Mathematics, UiO and Big Insight

Location: 
Domus Medica, room 2240 (Dep. of Biochemistry)

Wednesday Lunch: Einar Broch Johnsen

Wednesday, March 15, 2017 - 12:00

Speaker: Einar Broch Johnsen, professor at the Department of Informatics, University of Oslo.

Title: Cloud computing: Predicting behavior using models.

Abstract: We discuss cloud computing and how semantic models can be used to predict the behavior of software running on the cloud.
Software on the cloud differs from regular software in that it can adapt its configuration elastically depending on its computational load.
The talk will be based on research from the EU FP7 project Envisage, coordinated by Johnsen 2014–2016.

The lunch starts at 12:00, and the talk will start around 12:20.

Location: 
Sverdrups plass (lunch area), 8th floor N. H. Abels hus, Department of Mathematics

THURSDAY Biostatistical Seminar: Hein Putter

Thursday, March 9, 2017 - 14:15

Speaker: Hein Putter, Proffesor, Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, The Netherlands

Title: Non-parametric estimation of transition probabilities in non-Markov multi-state models: the landmark Aalen-Johansen estimator

Coffee/tea served from 14:15 and the talk will start around 14:30.

Abstract: The topic non-parametric estimation of transition probabilities in non-Markov multi-state models has seen a remarkable surge of activity recently. Two recent papers have used the idea of subsampling in this context. The first paper, by de Uña Alvarez and Meira-Machado, uses a procedure based on (differences between) Kaplan–Meier estimators derived from a subset of the data consisting of all subjects observed to be in the given state at the given time. The second, by Titman, derived estimators of transition probabilities that are consistent in general non-Markov multi-state models. Here, we show that the same idea of subsampling, used in both these papers, combined with the Aalen–Johansen estimate of the state occupation probabilities derived from that subset, can also be used to obtain a relatively simple and intuitive procedure which we term landmark Aalen–Johansen. We show that the landmark Aalen–Johansen estimator yields a consistent estimator of the transition probabilities in general non-Markov multi-state models under the same conditions as needed for consistency of the Aalen–Johansen estimator of the state occupation probabilities. Simulation studies show that the landmark Aalen–Johansen estimator has good small sample properties and is slightly more efficient than the other estimators.

Organizer: Oslo Centre for Biostatistics and Epidemiology (OCBE), Research group in Statistics and Biostatistics, Dept. of Mathematics, UiO and Big Insight

Location: 
Domus Medica, room 2240 (Dep. of Biochemistry)