Description
Book SynopsisThe book aims to provide both comprehensive reviews of the classical methods and an introduction to new developments in medical statistics. The topics range from meta analysis, clinical trial design, causal inference, personalized medicine to machine learning and next generation sequence analysis. Since the publication of the first edition, there have been tremendous advances in biostatistics and bioinformatics. The new edition tries to cover as many important emerging areas and reflect as much progress as possible. Many distinguished scholars, who greatly advanced their research areas in statistical methodology as well as practical applications, also have revised several chapters with relevant updates and written new ones from scratch.The new edition has been divided into four sections, including, Statistical Methods in Medicine and Epidemiology, Statistical Methods in Clinical Trials, Statistical Genetics, and General Methods. To reflect the rise of modern statistical genetics as one of the most fertile research areas since the publication of the first edition, the brand new section on Statistical Genetics includes entirely new chapters reflecting the state of the art in the field.Although tightly related, all the book chapters are self-contained and can be read independently. The book chapters intend to provide a convenient launch pad for readers interested in learning a specific topic, applying the related statistical methods in their scientific research and seeking the newest references for in-depth research.
Table of ContentsHistory of Statistical Thinking; Data Description; Predictive Value of Prognostic Biomarkers; Personalized Medicine; Quality Control; Imaging; Cost-Effectiveness Analysis; Quality of Life; Meta Analysis; Infections Disease; Sampling Survey; Capture-Recapture; Disease Screening; Biopharmaceutical Research; Pharmacology and Pre-Clinical Study; Toxicology; Dose-Response Model; Confirmative Trial; Surrogate Marker; Adaptive Trial Design; Chinese Medicine; Copy Number Variation; Linkage Study; Next-Generation Sequencing; Population Genetics; GWAS; Causal Inference; Survival Analysis; Longitudinal Data Analysis; Local Smoothing and Nonparametric Regression; Dependent Data Analysis; Bayesian Statistics; Prior-Free Probabilistic Inference; Stochastic Process; Missing Data; Time Series; Tree Based Method and Artificial Neural Network.
