Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent. This book extends the field by allowing for multivariate times. Applications where such data appear are survival of twins, survival of married couples and families, time to failure of right and left kidney for diabetic patients, life history data with time to outbreak of disease, complications and death, recurrent episodes of diseases and cross-over...
Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previou...
We hope this book will make the bewildering variety of methods for estimat- ing the abundance of animal populations more accessible to the uninitiated and more coherent to the cogniscenti. We have tried to emphasize the fun- damental similarity of many methods and to draw out the common threads that underlie them. With the exception of Chapter 13, we restrict ourselves to closed populations (those that do not change in composition over the period(s) being considered). Open population methods are in many ways simply extensions of closed population methods, and we have tried to pro- vide the...
We hope this book will make the bewildering variety of methods for estimat- ing the abundance of animal populations more accessible to the uninitiated...
Time-to-event data are ubiquitous in fields such as medicine, biology, demography, sociology, economics and reliability theory. Recently, a need to analyze more complex event histories has emerged. Examples are individuals that move among several states, frailty that makes some units fail before others, internal time-dependent covariates, and the estimation of causal effects from observational data.
The aim of this book is to bridge the gap between standard textbook models and a range of models where the dynamic structure of the data manifests itself fully. The common denominator of...
Time-to-event data are ubiquitous in fields such as medicine, biology, demography, sociology, economics and reliability theory. Recently, a need to...
Over the last ten years the introduction of computer intensive statistical methods has opened new horizons concerning the probability models that can be fitted to genetic data, the scale of the problems that can be tackled and the nature of the questions that can be posed. In particular, the application of Bayesian and likelihood methods to statistical genetics has been facilitated enormously by these methods. Techniques generally referred to as Markov chain Monte Carlo (MCMC) have played a major role in this process, stimulating synergies among scientists in different fields, such as...
Over the last ten years the introduction of computer intensive statistical methods has opened new horizons concerning the probability models that can ...
Prediction models are important in various fields, including medicine, physics, meteorology, and finance. Prediction models will become more relevant in the medical field with the increase in knowledge on potential predictors of outcome, e.g. from genetics. Also, the number of applications will increase, e.g. with targeted early detection of disease, and individualized approaches to diagnostic testing and treatment. The current era of evidence-based medicine asks for an individualized approach to medical decision-making. Evidence-based medicine has a central place for meta-analysis to...
Prediction models are important in various fields, including medicine, physics, meteorology, and finance. Prediction models will become more relevant ...
The study of brain function is one of the most fascinating pursuits of m- ern science. Functional neuroimaging is an important component of much of the current research in cognitive, clinical, and social psychology. The exci- ment of studying the brain is recognized in both the popular press and the scienti?c community. In the pages of mainstream publications, including The New York Times and Wired, readers can learn about cutting-edge research into topics such as understanding how customers react to products and - vertisements ("If your brain has a 'buy button, ' what pushes it?," The New...
The study of brain function is one of the most fascinating pursuits of m- ern science. Functional neuroimaging is an important component of much of th...
The purpose of this book is to examine the etiology of cancer in large human populations using mathematical models developed from an inter-disciplinary perspective of the population epidemiological, biodemographic, genetic and physiological basis of the mechanisms of cancer initiation and progression. In addition an investigation of how the basic mechanism of tumor initiation relates to general processes of senescence and to other major chronic diseases (e.g., heart disease and stroke) will be conducted.
The purpose of this book is to examine the etiology of cancer in large human populations using mathematical models developed from an inter-discipli...
Bias analysis quantifies the influence of systematic error on an epidemiology study's estimate of association. The fundamental methods of bias analysis in epi- miology have been well described for decades, yet are seldom applied in published presentations of epidemiologic research. More recent advances in bias analysis, such as probabilistic bias analysis, appear even more rarely. We suspect that there are both supply-side and demand-side explanations for the scarcity of bias analysis. On the demand side, journal reviewers and editors seldom request that authors address systematic error aside...
Bias analysis quantifies the influence of systematic error on an epidemiology study's estimate of association. The fundamental methods of bias analysi...
DNA microarrays are an important technology for studying gene expression. With a single hybridization, the level of expression of thousands of genes, or even an entire genome, can be estimated for a sample of cells. Consequently, manylaboratoriesareattemptingtoutilizeDNAmicroarraysintheirresearch. Whereaslaboratoriesarewellpreparedtoaddressthesigni?cantexperimental challenges in obtaining reproducible data from this RNA-based assay, inv- tigators are less prepared to analyze the large volumes of data produced by DNA microarrays. Although many software packages have been developed for the...
DNA microarrays are an important technology for studying gene expression. With a single hybridization, the level of expression of thousands of genes, ...
Quantitative trait locus (QTL) mapping is used to discover the genetic and molecular architecture underlying complex quantitative traits. It has important applications in agricultural, evolutionary, and biomedical research. R/qtl is an extensible, interactive environment for QTL mapping in experimental crosses. It is implemented as a package for the widely used open source statistical software R and contains a diverse array of QTL mapping methods, diagnostic tools for ensuring high-quality data, and facilities for the fit and exploration of multiple-QTL models, including QTL x QTL and QTL...
Quantitative trait locus (QTL) mapping is used to discover the genetic and molecular architecture underlying complex quantitative traits. It has im...
