This book covers a wide range of recent statistical methods that are of interest to scientists in biostatistics as well as in other related fields such as chemometrics, environmetrics and geophysics. The contributed papers, from internationally recognized researchers, present various statistical methodologies together with a selected scope of their main mathematical properties and their application in a real case study.

Data collected in psychiatry and related fields are complex because outcomes are rarely directly observed, there are multiple correlated repeated measures within individuals, there is natural heterogeneity in treatment responses and in other characteristics in the populations. Simple statistical methods do not work well with such data. More advanced statistical methods capture the data complexity better, but are difficult to apply appropriately and correctly by investigators who do not have advanced training in statistics. This book presents, at a non-technical level, several approaches for the analysis of correlated data: mixed models for continuous and categorical outcomes, nonparametric methods for repeated measures and growth mixture models for heterogeneous trajectories over time. Separate chapters are devoted to techniques for multiple comparison correction, analysis in the presence of missing data, adjustment for covariates, assessment of mediator and moderator effects, study design and sample size considerations. The focus is on the assumptions of each method, applicability and interpretation rather than on technical details. Features Provides an overview of intermediate to advanced statistical methods applied to psychiatry. Takes a non-technical approach with mathematical details kept to a minimum. Includes lots of detailed examples from published studies in psychiatry and related fields. Software programs, data sets and output are available on a supplementary website. The intended audience are applied researchers with minimal knowledge of statistics, although the book could also benefit collaborating statisticians. The book, together with the online materials, is a valuable resource aimed at promoting the use of appropriate statistical methods for the analysis of repeated measures data. Ralitza Gueorguieva is a Senior Research Scientist at the Department of Biostatistics, Yale School of Public Health. She has more than 20 years experience in statistical methodology development and collaborations with psychiatrists and other researchers, and is the author of over 130 peer-reviewed publications.

Dieser Band behandelt eine Reihe statistischer Themen, die bei der Analyse biologischer und medizinischer Daten allgemein Anwendung finden. Diese 2. Auflage wurde komplett überarbeitet, aktualisiert und erweitert. Einige Kapitel sind neu hinzugekommen, u.a. zur multiplen linearen Regression in der biomedizinischen Forschung. Der Stoff ist so gegliedert, dass der Leser den Text unabhängig von der jeweiligen statistischen Methode leicht nach Problemstellungen durchsuchen kann. Mit zahlreichen durchgearbeiteten Beispielen, die detaillierte Lösungsangaben zu Problemen aus der Praxis liefern.

The ability to analyze and interpret enormous amounts of data has become a prerequisite for success in allied healthcare and the health sciences. Now in its 11th edition, Biostatistics: A Foundation for Analysis in the Health Sciences continues to offer in-depth guidance toward biostatistical concepts, techniques, and practical applications in the modern healthcare setting. Comprehensive in scope yet detailed in coverage, this text helps students understand—and appropriately use—probability distributions, sampling distributions, estimation, hypothesis testing, variance analysis, regression, correlation analysis, and other statistical tools fundamental to the science and practice of medicine. Clearly-defined pedagogical tools help students stay up-to-date on new material, and an emphasis on statistical software allows faster, more accurate calculation while putting the focus on the underlying concepts rather than the math. Students develop highly relevant skills in inferential and differential statistical techniques, equipping them with the ability to organize, summarize, and interpret large bodies of data. Suitable for both graduate and advanced undergraduate coursework, this text retains the rigor required for use as a professional reference.

Essential Statistical Methods for Medical Statistics presents only key contributions which have been selected from the volume in the Handbook of Statistics: Medical Statistics, Volume 27 (2009). While the use of statistics in these fields has a long and rich history, the explosive growth of science in general, and of clinical and epidemiological sciences in particular, has led to the development of new methods and innovative adaptations of standard methods. This volume is appropriately focused for individuals working in these fields. Contributors are internationally renowned experts in their respective areas. Contributors are internationally renowned experts in their respective areas Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research Methods for assessing Biomarkers, analysis of competing risks Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs Structural equations modelling and longitudinal data analysis

The book brings together experts working in public health and multi-disciplinary areas to present recent issues in statistical methodological development and their applications. This timely book will impact model development and data analyses of public health research across a wide spectrum of analysis. Data and software used in the studies are available for the reader to replicate the models and outcomes. The fifteen chapters range in focus from techniques for dealing with missing data with Bayesian estimation, health surveillance and population definition and implications in applied latent class analysis, to multiple comparison and meta-analysis in public health data. Researchers in biomedical and public health research will find this book to be a useful reference and it can be used in graduate level classes.

Like its two successful previous editions, Health & Numbers: A Problems-Based Introduction to Biostatistics, Third Edition, is the only fully problems-based introduction to biostatistics and offers a concise introduction to basic statistical concepts and reasoning at a level suitable for a broad spectrum of students and professionals in medicine and the allied health fields. This book has always been meant for use by advanced students who have not previously had an introductory biostatistics course - material often presented in a one-semester course - or by busy professionals who need to learn the basics of biostatistics. This user-friendly resource features over 200 real-life examples and real data to discuss and teach fundamental statistical methods. The new edition offers even more exercises than the second edition, and features enhanced Microsoft Excel and SAS samples and examples. Health & Numbers, Third Edition, truly strikes a balance between principles and methods of calculation that is particularly useful for students in medicine and health-related fields who need to know biostatistics.