Integration of Multiple Genomic Data Sources in a Bayesian Cox Model for Variable Selection and Prediction.

Bayesian variable selection becomes more and more important in statistical analyses, in particular when performing variable selection in high dimensions. For survival time models and in the presence of genomic data, the state of the art is still quite unexploited. One of the more recent approaches suggests a Bayesian semiparametric proportional hazards model for right censored time-to-event data.

Lung cancer risk among bakers, pastry cooks and confectionary makers: the SYNERGY study.

Introduction: Some studies have suggested increased lung cancer risks among bakers, however the results overall were inconsistent. The authors studied lung cancer risks among bakers and baking-related occupations in the SYNERGY pooled case-control database from 16 countries.

Methods: Occupation in a baking-related job was identified from the subjects' job histories.