Bayesian Analysis With Stata

Autore: John Thompson
Editore: Statacorp Lp
ISBN:
Grandezza: 43,27 MB
Formato: PDF, Docs
Vista: 9752
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Bayesian Analysis with Stata is a compendium of Stata user-written commands for Bayesian analysis.

Bayesian Applications In Pharmaceutical Development

Autore: Mani Lakshminarayanan
Editore: CRC Press
ISBN: 1351584170
Grandezza: 25,64 MB
Formato: PDF, Mobi
Vista: 9747
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The cost for bringing new medicine from discovery to market has nearly doubled in the last decade and has now reached $2.6 billion. There is an urgent need to make drug development less time-consuming and less costly. Innovative trial designs/ analyses such as the Bayesian approach are essential to meet this need. This book will be the first to provide comprehensive coverage of Bayesian applications across the span of drug development, from discovery, to clinical trial, to manufacturing with practical examples. This book will have a wide appeal to statisticians, scientists, and physicians working in drug development who are motivated to accelerate and streamline the drug development process, as well as students who aspire to work in this field. The advantages of this book are: Provides motivating, worked, practical case examples with easy to grasp models, technical details, and computational codes to run the analyses Balances practical examples with best practices on trial simulation and reporting, as well as regulatory perspectives Chapters written by authors who are individual contributors in their respective topics Dr. Mani Lakshminarayanan is a researcher and statistical consultant with more than 30 years of experience in the pharmaceutical industry. He has published over 50 articles, technical reports, and book chapters besides serving as a referee for several journals. He has a PhD in Statistics from Southern Methodist University, Dallas, Texas and is a Fellow of the American Statistical Association. Dr. Fanni Natanegara has over 15 years of pharmaceutical experience and is currently Principal Research Scientist and Group Leader for the Early Phase Neuroscience Statistics team at Eli Lilly and Company. She played a key role in the Advanced Analytics team to provide Bayesian education and statistical consultation at Eli Lilly. Dr. Natanegara is the chair of the cross industry-regulatory-academic DIA BSWG to ensure that Bayesian methods are appropriately utilized for design and analysis throughout the drug-development process.

Applied Regression Analysis

Autore: Christer Thrane
Editore: Routledge
ISBN: 0429813031
Grandezza: 16,34 MB
Formato: PDF, Mobi
Vista: 9353
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This book is an introduction to regression analysis, focusing on the practicalities of doing regression analysis on real-life data. Contrary to other textbooks on regression, this book is based on the idea that you do not necessarily need to know much about statistics and mathematics to get a firm grip on regression and perform it to perfection. This non-technical point of departure is complemented by practical examples of real-life data analysis using statistics software such as Stata, R and SPSS. Parts 1 and 2 of the book cover the basics, such as simple linear regression, multiple linear regression, how to interpret the output from statistics programs, significance testing and the key regression assumptions. Part 3 deals with how to practically handle violations of the classical linear regression assumptions, regression modeling for categorical y-variables and instrumental variable (IV) regression. Part 4 puts the various purposes of, or motivations for, regression into the wider context of writing a scholarly report and points to some extensions to related statistical techniques. This book is written primarily for those who need to do regression analysis in practice, and not only to understand how this method works in theory. The book’s accessible approach is recommended for students from across the social sciences.

Handbook Of Meta Analysis In Ecology And Evolution

Autore: Julia Koricheva
Editore: Princeton University Press
ISBN: 0691137293
Grandezza: 23,72 MB
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Vista: 8512
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Meta-analysis is a powerful statistical methodology for synthesizing research evidence across independent studies. This is the first comprehensive handbook of meta-analysis written specifically for ecologists and evolutionary biologists, and it provides an invaluable introduction for beginners as well as an up-to-date guide for experienced meta-analysts. The chapters, written by renowned experts, walk readers through every step of meta-analysis, from problem formulation to the presentation of the results. The handbook identifies both the advantages of using meta-analysis for research synthesis and the potential pitfalls and limitations of meta-analysis (including when it should not be used). Different approaches to carrying out a meta-analysis are described, and include moment and least-square, maximum likelihood, and Bayesian approaches, all illustrated using worked examples based on real biological datasets. This one-of-a-kind resource is uniquely tailored to the biological sciences, and will provide an invaluable text for practitioners from graduate students and senior scientists to policymakers in conservation and environmental management. Walks you through every step of carrying out a meta-analysis in ecology and evolutionary biology, from problem formulation to result presentation Brings together experts from a broad range of fields Shows how to avoid, minimize, or resolve pitfalls such as missing data, publication bias, varying data quality, nonindependence of observations, and phylogenetic dependencies among species Helps you choose the right software Draws on numerous examples based on real biological datasets

Bayesian Methods In Health Economics

Autore: Gianluca Baio
Editore: CRC Press
ISBN: 1439895562
Grandezza: 63,85 MB
Formato: PDF, Kindle
Vista: 2328
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Health economics is concerned with the study of the cost-effectiveness of health care interventions. This book provides an overview of Bayesian methods for the analysis of health economic data. After an introduction to the basic economic concepts and methods of evaluation, it presents Bayesian statistics using accessible mathematics. The next chapters describe the theory and practice of cost-effectiveness analysis from a statistical viewpoint, and Bayesian computation, notably MCMC. The final chapter presents three detailed case studies covering cost-effectiveness analyses using individual data from clinical trials, evidence synthesis and hierarchical models and Markov models. The text uses WinBUGS and JAGS with datasets and code available online.

An Introduction To Generalized Linear Models

Autore: Annette J. Dobson
Editore: CRC Press
ISBN: 1584889519
Grandezza: 21,32 MB
Formato: PDF, ePub, Mobi
Vista: 3109
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Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis. Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods. Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons. It includes examples and exercises with complete data sets for nearly all the models covered.

Exam Prep For Biostatistics In Public Health Using Stata

Autore:
Editore:
ISBN:
Grandezza: 46,78 MB
Formato: PDF, Mobi
Vista: 679
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Applied Mixed Models In Medicine

Autore: Helen Brown
Editore: John Wiley & Sons
ISBN: 1118778243
Grandezza: 51,55 MB
Formato: PDF, ePub, Mobi
Vista: 7569
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A fully updated edition of this key text on mixed models, focusing on applications in medical research The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry. A mixed model allows the incorporation of both fixed and random variables within a statistical analysis, enabling efficient inferences and more information to be gained from the data. There have been many recent advances in mixed modelling, particularly regarding the software and applications. This third edition of Brown and Prescott’s groundbreaking text provides an update on the latest developments, and includes guidance on the use of current SAS techniques across a wide range of applications. Presents an overview of the theory and applications of mixed models in medical research, including the latest developments and new sections on incomplete block designs and the analysis of bilateral data. Easily accessible to practitioners in any area where mixed models are used, including medical statisticians and economists. Includes numerous examples using real data from medical and health research, and epidemiology, illustrated with SAS code and output. Features the new version of SAS, including new graphics for model diagnostics and the procedure PROC MCMC. Supported by a website featuring computer code, data sets, and further material. This third edition will appeal to applied statisticians working in medical research and the pharmaceutical industry, as well as teachers and students of statistics courses in mixed models. The book will also be of great value to a broad range of scientists, particularly those working in the medical and pharmaceutical areas.

Meta Analysis Oxford Bibliographies Online Research Guide

Autore: Oxford University Press
Editore: Oxford University Press, USA
ISBN: 9780199802517
Grandezza: 57,95 MB
Formato: PDF, ePub
Vista: 1178
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This ebook is a selective guide designed to help scholars and students of social work find reliable sources of information by directing them to the best available scholarly materials in whatever form or format they appear from books, chapters, and journal articles to online archives, electronic data sets, and blogs. Written by a leading international authority on the subject, the ebook provides bibliographic information supported by direct recommendations about which sources to consult and editorial commentary to make it clear how the cited sources are interrelated related. A reader will discover, for instance, the most reliable introductions and overviews to the topic, and the most important publications on various areas of scholarly interest within this topic. In social work, as in other disciplines, researchers at all levels are drowning in potentially useful scholarly information, and this guide has been created as a tool for cutting through that material to find the exact source you need. This ebook is a static version of an article from Oxford Bibliographies Online: Social Work, a dynamic, continuously updated, online resource designed to provide authoritative guidance through scholarship and other materials relevant to the study and practice of social work. Oxford Bibliographies Online covers most subject disciplines within the social science and humanities, for more information visit www.aboutobo.com.

Analysis Of Variance For Random Models

Autore: Hardeo Sahai
Editore: Springer Science & Business Media
ISBN: 9780817632304
Grandezza: 68,46 MB
Formato: PDF, Kindle
Vista: 6479
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Analysis of variance (ANOVA) models have become widely used tools and play a fundamental role in much of the application of statistics today. In particular, ANOVA models involving random effects have found widespread application to experimental design in a variety of fields requiring measurements of variance, including agriculture, biology, animal breeding, applied genetics, econometrics, quality control, medicine, engineering, and social sciences. This two-volume work is a comprehensive presentation of different methods and techniques for point estimation, interval estimation, and tests of hypotheses for linear models involving random effects. Both Bayesian and repeated sampling procedures are considered. Volume I examines models with balanced data (orthogonal models); Volume II studies models with unbalanced data (nonorthogonal models). Features and Topics: * Systematic treatment of the commonly employed crossed and nested classification models used in analysis of variance designs * Detailed and thorough discussion of certain random effects models not commonly found in texts at the introductory or intermediate level * Numerical examples to analyze data from a wide variety of disciplines * Many worked examples containing computer outputs from standard software packages such as SAS, SPSS, and BMDP for each numerical example * Extensive exercise sets at the end of each chapter * Numerous appendices with background reference concepts, terms, and results * Balanced coverage of theory, methods, and practical applications * Complete citations of important and related works at the end of each chapter, as well as an extensive general bibliography Accessible to readers with only a modest mathematical and statistical background, the work will appeal to a broad audience of students, researchers, and practitioners in the mathematical, life, social, and engineering sciences. It may be used as a textbook in upper-level undergraduate and graduate courses, or as a reference for readers interested in the use of random effects models for data analysis.

Speaking Stata Graphics

Autore: Nicholas J. Cox
Editore: Stata Press
ISBN: 9781597181440
Grandezza: 52,51 MB
Formato: PDF, ePub, Mobi
Vista: 5117
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Speaking Stata Graphics is ideal for researchers who want to produce effective, publication-quality graphs. A compilation of articles from the popular Speaking Stata column by Nicholas J. Cox, this book provides valuable insights about Stata's built-in and user-written statistical-graphics commands.

Applied Logistic Regression

Autore: David W. Hosmer, Jr.
Editore: John Wiley & Sons
ISBN: 1118548353
Grandezza: 76,14 MB
Formato: PDF, ePub, Mobi
Vista: 9205
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A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.

Longitudinal And Panel Data

Autore: Edward W. Frees
Editore: Cambridge University Press
ISBN: 9780521535380
Grandezza: 36,20 MB
Formato: PDF, ePub
Vista: 979
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An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.

A Guide To Econometrics

Autore: Peter Kennedy
Editore: MIT Press
ISBN: 9780262611831
Grandezza: 74,26 MB
Formato: PDF, ePub
Vista: 2895
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A popular, intuitively based overview of econometrics.

Contingency Table Analysis

Autore: Maria Kateri
Editore: Springer
ISBN: 0817648119
Grandezza: 58,75 MB
Formato: PDF, Docs
Vista: 9502
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Contingency tables arise in diverse fields, including life sciences, education, social and political sciences, notably market research and opinion surveys. Their analysis plays an essential role in gaining insight into structures of the quantities under consideration and in supporting decision making. Combining both theory and applications, this book presents models and methods for the analysis of two- and multidimensional-contingency tables. An excellent reference for advanced undergraduates, graduate students, and practitioners in statistics as well as biosciences, social sciences, education, and economics, the work may also be used as a textbook for a course on categorical data analysis. Prerequisites include basic background on statistical inference and knowledge of statistical software packages.

Statistical Meta Analysis Using R And Stata

Autore: Ding-Geng (Din) Chen
Editore: CRC Press
ISBN: 9780367183837
Grandezza: 59,44 MB
Formato: PDF, Kindle
Vista: 7476
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Applied Meta-Analysis Using R and Stata, Second Edition provides a thorough presentation of statistical meta-analyses (MA) with step-by-step implementations using R/Stata. The authors develop analysis step-by-step using appropriate R/Stata functions, which enables readers to gain an understanding of meta-analysis methods and R/Stata implementation so that they can use these two popular software packages to analyze their own meta-data. Each chapter gives examples of real studies compiled from the literature. After presenting the data and necessary background for understanding the applications, various methods for analyzing meta-data are introduced. The authors then develop analysis code using the appropriate R/Stata packages and functions. What's New in the Second Edition Adds Stata programs along with the R programs for Meta-Analysis. Updates all the statistical Meta-Analyses with R/Stata programs. Covers fixed-effects and random-effects MA, meta-regression, MA with rare-event, and MA-IPD vs. MA-SS. Adds five new chapters on multivariate MA, publication bias, missing data in MA, MA in evaluating diagnostic accuracy, and network MA. Suitable as a graduate-level text for a meta-data analysis course, the book is also a valuable reference for practitioners and biostatisticians (even those with little or no experience in using R or Stata) in public health, medical research, governmental agencies, and the pharmaceutical industry.

The Oxford Handbook Of Health Economics

Autore: Sherry Glied
Editore: Oxford University Press
ISBN: 0199238820
Grandezza: 59,55 MB
Formato: PDF, Mobi
Vista: 3147
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This book provides an engaging, comprehensive review of health economics, with a focus on policy implications in the developed and developing world. Authoritative, but non-technical, it stresses the wide reach of the discipline - across nations, health systems, and areas within health and medical care.

Causal Inference For Statistics Social And Biomedical Sciences

Autore: Guido W. Imbens
Editore: Cambridge University Press
ISBN: 1316094391
Grandezza: 46,46 MB
Formato: PDF, Mobi
Vista: 5430
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Most questions in social and biomedical sciences are causal in nature: what would happen to individuals, or to groups, if part of their environment were changed? In this groundbreaking text, two world-renowned experts present statistical methods for studying such questions. This book starts with the notion of potential outcomes, each corresponding to the outcome that would be realized if a subject were exposed to a particular treatment or regime. In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular subject. The authors discuss how randomized experiments allow us to assess causal effects and then turn to observational studies. They lay out the assumptions needed for causal inference and describe the leading analysis methods, including matching, propensity-score methods, and instrumental variables. Many detailed applications are included, with special focus on practical aspects for the empirical researcher.

The Frailty Model

Autore: Luc Duchateau
Editore: Springer Science & Business Media
ISBN: 038772835X
Grandezza: 24,97 MB
Formato: PDF, Kindle
Vista: 2644
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Readers will find in the pages of this book a treatment of the statistical analysis of clustered survival data. Such data are encountered in many scientific disciplines including human and veterinary medicine, biology, epidemiology, public health and demography. A typical example is the time to death in cancer patients, with patients clustered in hospitals. Frailty models provide a powerful tool to analyze clustered survival data. In this book different methods based on the frailty model are described and it is demonstrated how they can be used to analyze clustered survival data. All programs used for these examples are available on the Springer website.

Statistical Methods In Diagnostic Medicine

Autore: Xiao-Hua Zhou
Editore: John Wiley & Sons
ISBN: 1118626044
Grandezza: 13,27 MB
Formato: PDF, Mobi
Vista: 463
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Praise for the First Edition " . . . the book is a valuable addition to the literature in thefield, serving as a much-needed guide for both clinicians andadvanced students."—Zentralblatt MATH A new edition of the cutting-edge guide to diagnostic tests inmedical research In recent years, a considerable amount of research has focusedon evolving methods for designing and analyzing diagnostic accuracystudies. Statistical Methods in Diagnostic Medicine, Second Editioncontinues to provide a comprehensive approach to the topic, guidingreaders through the necessary practices for understanding thesestudies and generalizing the results to patient populations. Following a basic introduction to measuring test accuracy andstudy design, the authors successfully define various measures ofdiagnostic accuracy, describe strategies for designing diagnosticaccuracy studies, and present key statistical methods forestimating and comparing test accuracy. Topics new to the SecondEdition include: Methods for tests designed to detect and locate lesions Recommendations for covariate-adjustment Methods for estimating and comparing predictive values andsample size calculations Correcting techniques for verification and imperfect standardbiases Sample size calculation for multiple reader studies when pilotdata are available Updated meta-analysis methods, now incorporating randomeffects Three case studies thoroughly showcase some of the questions andstatistical issues that arise in diagnostic medicine, with allassociated data provided in detailed appendices. A related web sitefeatures Fortran, SAS®, and R software packages so thatreaders can conduct their own analyses. Statistical Methods in Diagnostic Medicine, Second Edition is anexcellent supplement for biostatistics courses at the graduatelevel. It also serves as a valuable reference for clinicians andresearchers working in the fields of medicine, epidemiology, andbiostatistics.