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ZüKoSt: Seminar on Applied Statistics
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Autumn Semester 2016
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Date / Time | Speaker | Title | Location | |
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15 September 2016 16:15-17:00 |
Emmanuel Lesaffre L-BioStat, Leuven |
Modeling multivariate multilevel continuous responses with a hierarchical regression model for the mean and covariance matrix applied to a large nursing data set | HG G 19.1 | |
Abstract: We propose a novel multivariate multilevel model that expresses both the mean and covariance structure as a multivariate mixed effects model. We called this the multilevel covariance regression (MCR) model. Two versions of this model are presented. In the first version the covariance matrix of the multivariate response is allowed to depend on covariates and random effects. In this model the random effects of the covariance part are assumed to be independent of random effects of the mean structure. In the second model this assumption is relaxed by allowing the two types of random effects to be dependent. The motivating data set is obtained from the RN4CAST (Sermeus et al. 2011) FP7 project which involves 33,731 registered nurses in 2,169 nursing units in 486 hospitals in 12 European countries. As response we have taken the three classical burnout dimensions (Maslach and Jackson, 1981) extracted from a 22-item questionnaire, i.e. emotional exhaustion (EE), depersonalization (DP) and personal accomplishment (PA). There are four levels in the total data set: nurses, nursing units, hospitals and (for the whole data set) countries. The first model is applied to the total data set, while the second model is applied to only the Belgian part of the data. The two models address the following nurse research questions simultaneously: 1) how much variation of burnout could be explained by the level-specific fixed and random effects? 2) do the variances and correlations among burnout stay constant across level-specific characteristics and units at each level? The two models are explored with respect to their statistical properties, but are also compared on the Belgian part of the study. We opted for the Bayesian approach to estimate the parameters of the model. To this end we made use of the JAGS Markov chain Monte Carlo program through the R package rjags. | ||||
13 October 2016 16:15-17:00 |
Torsten Hothorn |
Understanding and Applying Transformation Models | HG G 19.1 | |
Abstract: Transformation models are a surprisingly large and useful class of models for conditional and also unconditional distributions. Many known transformation models, for example the Cox proportional hazards model or proportional odds logistic regression, have been known for decades in survival or categorical data analysis. The strong connections between these models and other commonly used procedures, for example normal or binary linear models, are not very well known. It is very stimulating, both from an intellectual and a practical point of view, to interpret such classical models as transformation models and therefore as models for describing distributions instead of means. We will look at a cascade ranging from very simple to rather complex unconditional and conditional transformation models theoretically and practically. The R add-on package "mlt" (Most Likely Transformations) allows fitting many of such transformation models in the maximum likelihood framework and will be used to illustrate how one can estimate and analyse interesting transformations models in R. References http://dx.doi.org/10.1111/rssb.12017 http://arxiv.org/abs/1508.06749 http://CRAN.R-project.org/package=mlt https://cran.r-project.org/web/packages/mlt.docreg/vignettes/mlt.pdf | ||||
20 October 2016 16:15-17:00 |
Thomas Hofmann |
latent variable models for natural language understanding | HG G 19.1 | |
2 November 2016 16:15-17:00 |
Søren Højsgaard Aalborg University, DK |
Title T.B.A. | HG | |
Abstract: tba | ||||
8 December 2016 16:15-17:00 |
Nicolas Städler F. Hoffmann-La Roche Ltd, Basel |
Opportunities and Challenges of Statistics in Health Technology Assessment | HG G 19.1 | |
Abstract: Our aim at Roche is for every person who needs our products to be able to access and benefit from them. Market access, that is the coverage and reimbursement of our products by payers, is a crucial success factor in achieving this goal. As healthcare spendings are accelerating payers and public health authorities are carefully assessing benefits of new drugs over and above drugs already on the market. Health Technology Assessment (HTA) agencies have therefore adopted stringent product evaluation strategies and their expectations in terms of evidence on effectiveness of a new product very often exceed those required for regulatory approval. In this talk I will present work-in-progress examples where we use advanced statistics to inform robust payer evidence. Firstly, I will discuss surrogate endpoint validation and show how in some cases this is a useful approach to make predictions on how effects measured on biomarkers or on surrogate endpoints translate into effects which are considered payer relevant. Secondly, I will discuss network meta-analysis and explain how we used this approach in chronic lymphocytic leukemia to inform payers on the comparative effectiveness of our product to others on the market. I will further discuss our ideas on how to extend network meta-analysis to also include non-randomized trials. Finally, I will discuss extrapolation of survival curves as a key ingredient to calculate the so-called Incremental Cost Effectiveness Ratio (ICER) which serves many payers as an important reference value in their decision making. I will discuss the limitations of classical parametric extrapolation and I will show how we use advanced techniques based on mixture models to improve extrapolation and to obtain more accurate estimates of the ICER. |
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