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Frailty Models in Survival Analysis

Frailty Models in Survival Analysis Author Andreas Wienke
ISBN-10 1420073915
Release 2010-07-26
Pages 324
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The concept of frailty offers a convenient way to introduce unobserved heterogeneity and associations into models for survival data. In its simplest form, frailty is an unobserved random proportionality factor that modifies the hazard function of an individual or a group of related individuals. Frailty Models in Survival Analysis presents a comprehensive overview of the fundamental approaches in the area of frailty models. The book extensively explores how univariate frailty models can represent unobserved heterogeneity. It also emphasizes correlated frailty models as extensions of univariate and shared frailty models. The author analyzes similarities and differences between frailty and copula models; discusses problems related to frailty models, such as tests for homogeneity; and describes parametric and semiparametric models using both frequentist and Bayesian approaches. He also shows how to apply the models to real data using the statistical packages of R, SAS, and Stata. The appendix provides the technical mathematical results used throughout. Written in nontechnical terms accessible to nonspecialists, this book explains the basic ideas in frailty modeling and statistical techniques, with a focus on real-world data application and interpretation of the results. By applying several models to the same data, it allows for the comparison of their advantages and limitations under varying model assumptions. The book also employs simulations to analyze the finite sample size performance of the models.



Dynamical Biostatistical Models

Dynamical Biostatistical Models Author Daniel Commenges
ISBN-10 9781498729680
Release 2015-10-02
Pages 374
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Dynamical Biostatistical Models presents statistical models and methods for the analysis of longitudinal data. The book focuses on models for analyzing repeated measures of quantitative and qualitative variables and events history, including survival and multistate models. Most of the advanced methods, such as multistate and joint models, can be applied using SAS or R software. The book describes advanced regression models that include the time dimension, such as mixed-effect models, survival models, multistate models, and joint models for repeated measures and time-to-event data. It also explores the possibility of unifying these models through a stochastic process point of view and introduces the dynamic approach to causal inference. Drawing on much of their own extensive research, the authors use three main examples throughout the text to illustrate epidemiological questions and methodological issues. Readers will see how each method is applied to real data and how to interpret the results.



Mixed Effects Models for the Population Approach

Mixed Effects Models for the Population Approach Author Marc Lavielle
ISBN-10 9781482226508
Release 2014-07-14
Pages 383
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Wide-Ranging Coverage of Parametric Modeling in Linear and Nonlinear Mixed Effects Models Mixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools presents a rigorous framework for describing, implementing, and using mixed effects models. With these models, readers can perform parameter estimation and modeling across a whole population of individuals at the same time. Easy-to-Use Techniques and Tools for Real-World Data Modeling The book first shows how the framework allows model representation for different data types, including continuous, categorical, count, and time-to-event data. This leads to the use of generic methods, such as the stochastic approximation of the EM algorithm (SAEM), for modeling these diverse data types. The book also covers other essential methods, including Markov chain Monte Carlo (MCMC) and importance sampling techniques. The author uses publicly available software tools to illustrate modeling tasks. Methods are implemented in Monolix, and models are visually explored using Mlxplore and simulated using Simulx. Careful Balance of Mathematical Representation and Practical Implementation This book takes readers through the whole modeling process, from defining/creating a parametric model to performing tasks on the model using various mathematical methods. Statisticians and mathematicians will appreciate the rigorous representation of the models and theoretical properties of the methods while modelers will welcome the practical capabilities of the tools. The book is also useful for training and teaching in any field where population modeling occurs.



Exploring Studbooks for Wildlife Management and Conservation

Exploring Studbooks for Wildlife Management and Conservation Author F.P.G. Princée
ISBN-10 9783319500324
Release 2016-12-20
Pages 291
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Many endangered species of wild animals are managed in captivity through studbooks. In this book these data-rich resources are mined in innovative, integrated and statistically tested ways to maximise information gain for conservation practice – whether for captive or released/reintroduced or managed wild populations. This book is thus an important tool for all species managers, and for students and researchers in small population biology and wildlife conservation. The book's studbook analyses are grouped in three interrelated sections: natural history, demography and genetics. Statistical tests to determine the significance of results or to compare results between subgroups are undertaken throughout. Real studbooks of a variety of species, e.g. cranes, wolverines, blesbok, illustrate the practical applications and interpretations of the analyses and statistics. The “natural history” section presents analyses to determine baseline species information such as litter size, inter-birth interval, longevity and seasonality. “Demography” covers census(-style) analyses, age-class based life tables, comparative survival analyses and population projections. Solutions for dealing with small sample sizes are included.Inbreeding depression and unconscious selection form the main focus of the “genetics” section. Survival and life table analyses are used to assess inbreeding effects. Quantitative genetics methods are applied to natural history traits as a tool to monitor genetic variation. A fourth section on “conservation” shows how data from captive populations can be used where natural history data from wild populations are missing. A real example uses studbook data to inform Population Viability Analysis. The final section deals with issues related to incomplete and missing data and statistical topics. The purpose-written open-source software programs “Population Management Library (PML)” and “studbookR” used for analyses in the book, are available at www.princee.com.



Adaptive Design Methods in Clinical Trials Second Edition

Adaptive Design Methods in Clinical Trials  Second Edition Author Shein-Chung Chow
ISBN-10 9781439839881
Release 2011-12-01
Pages 374
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With new statistical and scientific issues arising in adaptive clinical trial design, including the U.S. FDA’s recent draft guidance, a new edition of one of the first books on the topic is needed. Adaptive Design Methods in Clinical Trials, Second Edition reflects recent developments and regulatory positions on the use of adaptive designs in clinical trials. It unifies the vast and continuously growing literature and research activities on regulatory requirements, scientific and practical issues, and statistical methodology. New to the Second Edition Along with revisions throughout the text, this edition significantly updates the chapters on protocol amendment and clinical trial simulation to incorporate the latest changes. It also includes five entirely new chapters on two-stage adaptive design, biomarker adaptive trials, target clinical trials, sample size and power estimation, and regulatory perspectives. Following in the tradition of its acclaimed predecessor, this second edition continues to offer an up-to-date resource for clinical scientists and researchers in academia, regulatory agencies, and the pharmaceutical industry. Written in an intuitive style at a basic mathematical and statistical level, the book maintains its practical approach with an emphasis on concepts via numerous examples and illustrations.



Demografie review for population research

Demografie  review for population research Author
ISBN-10 STANFORD:36105214188695
Release 2012
Pages
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Demografie review for population research has been writing in one form or another for most of life. You can find so many inspiration from Demografie review for population research also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Demografie review for population research book for free.



Interval Censored Time to Event Data

Interval Censored Time to Event Data Author Ding-Geng (Din) Chen
ISBN-10 9781466504257
Release 2012-07-19
Pages 434
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Interval-Censored Time-to-Event Data: Methods and Applications collects the most recent techniques, models, and computational tools for interval-censored time-to-event data. Top biostatisticians from academia, biopharmaceutical industries, and government agencies discuss how these advances are impacting clinical trials and biomedical research. Divided into three parts, the book begins with an overview of interval-censored data modeling, including nonparametric estimation, survival functions, regression analysis, multivariate data analysis, competing risks analysis, and other models for interval-censored data. The next part presents interval-censored methods for current status data, Bayesian semiparametric regression analysis of interval-censored data with monotone splines, Bayesian inferential models for interval-censored data, an estimator for identifying causal effect of treatment, and consistent variance estimation for interval-censored data. In the final part, the contributors use Monte Carlo simulation to assess biases in progression-free survival analysis as well as correct bias in interval-censored time-to-event applications. They also present adaptive decision making methods to optimize the rapid treatment of stroke, explore practical issues in using weighted logrank tests, and describe how to use two R packages. A practical guide for biomedical researchers, clinicians, biostatisticians, and graduate students in biostatistics, this volume covers the latest developments in the analysis and modeling of interval-censored time-to-event data. It shows how up-to-date statistical methods are used in biopharmaceutical and public health applications.



Computational Methods in Biomedical Research

Computational Methods in Biomedical Research Author Ravindra Khattree
ISBN-10 1420010921
Release 2007-12-12
Pages 432
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Continuing advances in biomedical research and statistical methods call for a constant stream of updated, cohesive accounts of new developments so that the methodologies can be properly implemented in the biomedical field. Responding to this need, Computational Methods in Biomedical Research explores important current and emerging computational statistical methods that are used in biomedical research. Written by active researchers in the field, this authoritative collection covers a wide range of topics. It introduces each topic at a basic level, before moving on to more advanced discussions of applications. The book begins with microarray data analysis, machine learning techniques, and mass spectrometry-based protein profiling. It then uses state space models to predict US cancer mortality rates and provides an overview of the application of multistate models in analyzing multiple failure times. The book also describes various Bayesian techniques, the sequential monitoring of randomization tests, mixed-effects models, and the classification rules for repeated measures data. The volume concludes with estimation methods for analyzing longitudinal data. Supplying the knowledge necessary to perform sophisticated statistical analyses, this reference is a must-have for anyone involved in advanced biomedical and pharmaceutical research. It will help in the quest to identify potential new drugs for the treatment of a variety of diseases.