Bøger i Statistics for Biology and Health serien

This very popular textbook, now in its third edition, offers an accessible description of fundamental and more advanced concepts and methods of logistic regression. This edition includes three new chapters and an expanded section about modeling guidelines.

Applied statisticians in many fields must frequently analyze time to event data. While the statistical tools presented in this book are applicable to data from medicine, biology, public health, epidemiology, engineering, economics, and demography, the focus here is on applications of the techniques to biology and medicine. The analysis of survival experiments is complicated by issues of censoring, where an individual's life length is known to occur only in a certain period of time, and by truncation, where individuals enter the study only if they survive a sufficient length of time or individuals are included in the study only if the event has occurred by a given date. The use of counting process methodology has allowed for substantial advances in the statistical theory to account for censoring and truncation in survival experiments. This book makes these complex methods more accessible to applied researchers without an advanced mathematical background. The authors present the essence of these techniques, as well as classical techniques not based on counting processes, and apply them to data. Practical suggestions for implementing the various methods are set off in a series of Practical Notes at the end of each section. Technical details of the derivation of the techniques are sketched in a series of Technical Notes. This book will be useful for investigators who need to analyze censored or truncated life time data, and as a textbook for a graduate course in survival analysis. The prerequisite is a standard course in statistical methodology. "e;This book...offers an excellent course in survival analysis for

Clustered survival data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. Frailty models provide a powerful tool to analyse clustered survival data. In contrast to the large number of research publications on frailty models, relatively few statistical software packages contain frailty models.It is demanding for statistical practitioners and graduate students to grasp a good knowledge on frailty models from the existing literature. This book provides an in-depth discussion and explanation of the basics of frailty model methodology for such readers. The discussion includes parametric and semiparametric frailty models and accelerated failure time models. Common techniques to fit frailty models include the EM-algorithm, penalised likelihood techniques, Laplacian integration and Bayesian techniques. More advanced frailty models for hierarchical data are also included.Real-lifeexamples are used to demonstrate how particular frailty models can be fitted and how the results should be interpreted. The programs to fit all the worked-out examples in the book are available from the Springer website with most of the programs developed in the freeware packages R and Winbugs. The book starts with a brief overview of some basic concepts in classical survival analysis, collecting what is needed for the reading on the more complex frailty models.

This is a book about regression analysis, that is, the situation in statistics where the distribution of a response (or outcome) variable is related to - planatory variables (or covariates). This is an extremely common situation in the application of statistical methods in many ?elds, andlinear regression,- gistic regression, and Cox proportional hazards regression are frequently used for quantitative, binary, and survival time outcome variables, respectively. Several books on these topics have appeared and for that reason one may well ask why we embark on writing still another book on regression. We have two main reasons for doing this: 1. First, we want to highlightsimilaritiesamonglinear,logistic,proportional hazards,andotherregressionmodelsthatincludealinearpredictor. These modelsareoftentreatedentirelyseparatelyintextsinspiteofthefactthat alloperationsonthemodelsdealingwiththelinearpredictorareprecisely the same, including handling of categorical and quantitative covariates, testing for linearity and studying interactions. 2. Second, we want to emphasize that, for any type of outcome variable, multiple regression models are composed of simple building blocks that areaddedtogetherinthelinearpredictor:thatis,t-tests,one-wayanalyses of variance and simple linear regressions for quantitative outcomes, 2 2k+1) tables and simple logistic regressions for binary outcomes, and 2-and (k+1)-sample logrank testsand simple Cox regressionsfor survival data. Thishastwoconsequences. Allthesesimpleandwellknownmethods can be considered as special cases of the regression models. On the other hand, the e?ect of a single explanatory variable in a multiple regression model can be interpreted in a way similar to that obtained in the simple analysis, however, now valid only for the other explanatory variables in the model "e;held ?xed"e;.

Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods.

The first accessible introduction to the many various wildlife assessment methods! This book uses a new approach that makes the full range of methods accessible in a way that has not previously been possible.

Despite advances in statistical approaches towards clinical outcome prediction, these innovations are insufficiently utilized in medical research. This book provides information on how modern statistical concepts and regression methods can be applied.

This book offers an introduction to epidemiology suitable for Medical and Public Health Schools in developing and transition countries and in workshops in these countries, based on examples and practical work drawn from the present state of health in Vietnam.

This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions.

This book examines statistical techniques that are critically important to Chemistry, Manufacturing, and Control (CMC) activities.

This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. The book also provides a comparison of adaptive modeling to generalized additive modeling (GAM) and multiple adaptive regression splines (MARS) for univariate outcomes.

This book surveys statistical aspects of designing, analyzing and interpreting results of genome-wide association scans for genetic causes of disease, using unrelated subjects. Covers bioinformatics and data handling methods needed to ready data for analysis.

This book offers an introduction to epidemiology suitable for Medical and Public Health Schools in developing and transition countries and in workshops in these countries, based on examples and practical work drawn from the present state of health in Vietnam.

This book reviews current statistical methods for inferring residual life distribution, including inference methods for mean and median, or quantile, residual life analysis through medical data examples. The concept is extended to competing risks analysis.

The first comprehensive examination of an emerging topic, this text complements existing resources on recurrent event data, and includes detailed treatment of regression, parametric and non-parametric methodologies, and mathematical calculations.

The analysis of gene expression profile data from DNA micorarray studies are discussed in this book. It includes an Appendix tutorial on the use of BRB-ArrayTools and step by step analyses of several major datasets using this software which is available from the National Cancer Institute.

This reference book for the analysis of genetic association studies makes an ideal companion to graduate-level students. In addition to providing derivations, the book deploys real examples and simulations to illustrate its step-by-step applications.

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.

This book is a primer for readers interested in learning more about this fascinating subject and the many statistical challenges inherent in functional neuroimaging data. It presents the basics of technique and surveys popular statistical approaches.

This text bridges the gap between standard models, and those where the dynamic structure of the data manifests itself fully. The common thread is stochastic processes. The authors show how martingales and stochastic integrals fit with censored data.

This fresh edition, substantially revised and augmented, provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics. The examples used, analyzed using Stata, can be applied to other areas.

Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine.

Examining the etiology of cancer in large human populations using mathematical models developed from an inter-disciplinary perspective, this book investigates how tumor initiation relates to general processes of senescence and to other major chronic diseases

This book offers statistical models and methods used to understand human genetics, focusing on modern approaches to association analysis. Numerous examples illustrate key points, and the text includes exercises for students with a broad range of skill levels.

This book details the statistical concepts used in gene mapping. It presents elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language.

This book collects and unifies statistical models and methods that have been proposed for analyzing interval-censored failure time data. It provides the first comprehensive coverage of the topic and complements the books on right-censored data.