ABS08 - 2008 Applied Bayesian Statistics School

BAYESIAN DECISION PROBLEMS IN BIOSTATISTICS AND CLINICAL TRIALS

Centro Congressi Panorama, Trento, Italy

9 - 13 June, 2008

PARTICIPANTS'S TALKS


Francesca Ieva, Politecnico di Milano, Italy

The Milano network for acute coronary syndromes and emergency services: a biostatistical case study

An important open problem in the study on in-hospital mortality and successfulness of reperfusion therapy for patients with ST-Elevation Myocardial Infarction (STEMI) is to point out the most significant prognostic factors which influence these outcomes. On the basis of a wide set of data collected in the urban area of Milano (in 2006-2008), the subject of my master thesis is the study of suitable statistical techniques to analyze these data (exact confidence interval for Odds Ratio, nonparametric estimation of Odds Ratio dependent on continuous covariates, nonlinear regression models, etc.) in order to gain useful results which could help and support medical analyses and strategic decisions.

Valeria Vitelli, Politecnico di Milano, Italy

k-means clustering and principal points: an open problem on functional data

The field of statistics has recently been strongly challenged, mainly in the biostatistics area, by the problem of performing new techniques to deal with functional data (growth curves, cardiovascular geometry, etc.); one of the most important aims of these techniques is to detect and classify different patterns, and eventually to elicit a representative element for each group. The open problem, subject of my master thesis, is to find in this functional framework the links between classical clustering algorithms (k-means, etc.) and the theory of principal points, already proved in the multivariate setting.

Tobia Sing, Novartis, Switzerland

Visualizing classifier performance in R using ROCR

ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R. It features over 25 performance measures that can be freely combined to create two-dimensional performance curves. Standard methods for investigating trade-offs between specific performance measures are available within a uniform framework, including receiver operating characteristic (ROC) graphs, precision/recall plots, lift charts and cost curves. ROCR integrates tightly with R's powerful graphics capabilities, thus allowing for highly adjustable plots. Being equipped with only three commands and reasonable default values for optional parameters, ROCR combines flexibility with ease of usage. AVAILABILITY: http://rocr.bioinf.mpi-sb.mpg.de. ROCR can be used under the terms of the GNU General Public License. Running within R, it is platform-independent.