The 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. 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.
Need help interpreting other people's health research? This book offers guidance for students undertaking a critical review of quantitative research papers and will also help health professionals to understand and interpret statistical results within health-related research papers. Walker and Almond have helpfully cross-referenced throughout, so those requiring in-depth explanations or additional worked examples can locate these easily. Interpreting Statistical Research Findings is key reading for nursing and health care students and will help make this area of research much easier to tackle!
Mathematical modeling is critical to our understanding of how infectious diseases spread at the individual and population levels. This book gives readers the necessary skills to correctly formulate and analyze mathematical models in infectious disease epidemiology, and is the first treatment of the subject to integrate deterministic and stochastic models and methods. This comprehensive and accessible book also features numerous detailed exercises throughout; full elaborations to all exercises are provided.
This volume is written using basic concepts and commonly used methods of design and analysis in medical statistics, incorporating the operation of statistical package SAS and 100 computer experiments for the important statistical phenomena related to each chapter. All necessary data, including reference answers for the exercises, SAS programs for all computer experiments and part of the examples, and data documents for 12 medical researches are available.
This long awaited second edition of this bestseller continues to provide a comprehensive, user friendly, down-to-earth guide to elementary statistics. The book presents a detailed account of the most important procedures for the analysis of data, from the calculation of simple proportions, to a variety of statistical tests, and the use of regression models for modeling of clinical outcomes. The level of mathematics is kept to a minimum to make the material easily accessible to the novice, and a multitude of illustrative cases are included in every chapter, drawn from the current research literature. The new edition has been completely revised and updated and includes new chapters on basic quantitative methods, measuring survival, measurement scales, diagnostic testing, bayesian methods, meta-analysis and systematic reviews.
Like its predecessors, the fourth edition of Risk Adjustment for Measuring Healthcare Outcomes presents the fundamental principles and concepts of risk adjustment for comparing outcomes of care and explains why risk adjustment is a critical tool for measuring quality and setting reimbursement rates. This book is a comprehensive guide to the issues raised by risk adjustment, including the pros and cons of different data sources, the validity and reliability of risk adjustment methods, the effects of various statistical modeling approaches, and concerns relating to special populations.
This comprehensive, graduate-level text provides state-of-the-art tools that facilitate the reading and interpretation of clinical research articles that use increasingly complex statistical techniques. It addresses clinically relevant topics in biostatistics beyond the usual introduction to linear models, such as survival analysis and evaluation of screening tests. The text emphasizes the importance of understanding the underlying logic of statistical inference and statistical models to support correct interpretation and effective translation into practice. It promotes appropriate statistical method selection for conducting translational research. With a focus on disseminating information in easily understandable language, the text addresses basic statistical reasoning and four different classes of statistical models. The appendix provides refreshers on the algebraic underpinnings of statistics. Chapters include examples from current research and multiple exercises designed to reinforce learning.