Bayesian statistics in oncology: a guide for the clinical investigator

The rise of evidence-based medicine as well as important progress in statistical methods and computational power have led to a second birth of the >200-year-old Bayesian framework. The use of Bayesian techniques, in particular in the design and interpretation of clinical trials, offers several substantial advantages over the classical statistical approach. First, in contrast to classical statistics, Bayesian analysis allows a direct statement regarding the probability that a treatment was beneficial. Second, Bayesian statistics allow the researcher to incorporate any prior information in the analysis of the experimental results. Third, Bayesian methods can efficiently handle complex statistical models, which are suited for advanced clinical trial designs. Finally, Bayesian statistics encourage a thorough consideration and presentation of the assumptions underlying an analysis, which enables the reader to fully appraise the authors' conclusions. Both Bayesian and classical statistics have their respective strengths and limitations and should be viewed as being complementary to each other; we do not attempt to make a head-to-head comparison, as this is beyond the scope of the present review. Rather, the objective of the present article is to provide a nonmathematical, reader-friendly overview of the current practice of Bayesian statistics coupled with numerous intuitive examples from the field of oncology. It is hoped that this educational review will be a useful resource to the oncologist and result in a better understanding of the scope, strengths, and limitations of the Bayesian approach.

Migrant Background and Higher Education Participation in Europe. The Effect of the Educational Systems

The main aim of this article is to shed light on the extent to which differences in higher education participation between people with and without a migrant background of low/higher social origin can be explained by two macro-level characteristics of national educational institutions: stratification of the secondary school system and provision of alternative access to higher education. General assumptions are that people with a migrant background of low social origin benefit in low-stratified secondary school systems and in systems that provide alternative access to institutions of higher education more than their native peers in the same social stratum, owing to primary and secondary effects of migrant background. Database is a pooled dataset of the five waves of the European Social Survey. Results of logistic multi-level analyses indicate that a low-stratified secondary school system improves the probability of people with a migrant background/low social origin attaining a higher education degree. On the other hand, a stratified secondary school system reduces their chances regarding this educational stage. The provision of alternative access to an institution of higher education improves their likelihood of becoming higher education graduates.