Biostatistics

Several recent advances in smoothing and semiparametric regression are presented in this book from a unifying, Bayesian perspective. Simulation-based full Bayesian Markov chain Monte Carlo (MCMC) inference, as well as empirical Bayes procedures closely related to penalized likelihood estimation and mixed models, are considered here. Throughout, the focus is on semiparametric regression and smoothing based on basis expansions of unknown functions and effects in combination with smoothness priors for the basis coefficients. Beginning with a review of basic methods for smoothing and mixed models, longitudinal data, spatial data, and event history data are treated in separate chapters. Worked examples from various fields such as forestry, development economics, medicine, and marketing are used to illustrate the statistical methods covered in this book. Most of these examples have been analysed using implementations in the Bayesian software, BayesX, and some with R Codes.

This book discusses novel advances in informatics and statistics in molecular cancer research. Through eight chapters it discusses specific topics in cancer research, talks about how the topics give rise to development of new informatics and statistics tools, and explains how the tools can be applied. The focus of the book is to provide an understanding of key concepts and tools, rather than focusing on technical issues. A main theme is the extensive use of array technologies in modern cancer research — gene expression and exon arrays, SNP and copy number arrays and methylation arrays — to derive quantitative and qualitative statements about cancer, its progression and aetiology, and to understand how these technologies at one hand allow us learn about cancer tissue as a complex system and at the other hand allow us to pinpoint key genes and events as crucial for the development of the disease. Cancer is characterized by genetic and genomic alterations that influence all levels of the cell's machinery and function.