Modern Issues and Methods in Biostatistics

This book introduces modern methods in biostatistics and helps researchers and students quickly grasp key concepts and methods. It details novel ingredients or developments in methodology, computation algorithms and applications.

Classic biostatistics, a branch of statistical science, has as its main focus the applications of statistics in public health, the life sciences, and the pharmaceutical industry. Modern biostatistics, beyond just a simple application of statistics, is a confluence of statistics and knowledge of multiple intertwined fields. The application demands, the advancements in computer technology, and the rapid growth of life science data (e. g. , genomics data) have promoted the formation of modern biostatistics. There are at least three characteristics of modern biostatistics: (1) in-depth engagement in the application fields that require penetration of knowledge across several fields, (2) high-level complexity of data because they are longitudinal, incomplete, or latent because they are heterogeneous due to a mixture of data or experiment types, because of high-dimensionality, which may make meaningful reduction impossible, or because of extremely small or large size; and (3) dynamics, the speed of development in methodology and analyses, has to match the fast growth of data with a constantly changing face.
This book is written for researchers, biostatisticians/statisticians, and scientists who are interested in quantitative analyses. The goal is to introduce modern methods in biostatistics and help researchers and students quickly grasp key concepts and methods. Many methods can solve the same problem and many problems can be solved by the same method, which becomes apparent when those topics are discussed in a single volume.

Multiple-Hypothesis Testing Strategy. - Pharmaceutical Decision and Game Theory. - Noninferiority Trial Design. - Adaptive Trial Design. - Missing Data Imputation and Analysis. - Multivariate and Multistage Survival Data Modeling. - Meta-analysis. - Data Mining and Signal Detection. - Monte Carlo Simulation. - Bayesian Methods and Applications. -