Download or read book Developing a Protocol for Observational Comparative Effectiveness Research A User s Guide written by Agency for Health Care Research and Quality (U.S.) and published by Government Printing Office. This book was released on 2013-02-21 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)

Download or read book Nonparametric Paired Tests for Censored Survival Data Incorporating Prognostic Covariate Information written by Shari Messinger Cayetano and published by . This book was released on 2002 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates written by Jeffrey R. Wilson and published by Springer Nature. This book was released on 2020-09-28 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides a concise point of research topics and reference for modeling correlated response data with time-dependent covariates, and longitudinal data for the analysis of population-averaged models, highlighting methods by a variety of pioneering scholars. While the models presented in the volume are applied to health and health-related data, they can be used to analyze any kind of data that contain covariates that change over time. The included data are analyzed with the use of both R and SAS, and the data and computing programs are provided to readers so that they can replicate and implement covered methods. It is an excellent resource for scholars of both computational and methodological statistics and biostatistics, particularly in the applied areas of health. ​

Download or read book Methods and Applications of Longitudinal Data Analysis written by Xian Liu and published by Elsevier. This book was released on 2015-09-01 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods and Applications of Longitudinal Data Analysis describes methods for the analysis of longitudinal data in the medical, biological and behavioral sciences. It introduces basic concepts and functions including a variety of regression models, and their practical applications across many areas of research. Statistical procedures featured within the text include: descriptive methods for delineating trends over time linear mixed regression models with both fixed and random effects covariance pattern models on correlated errors generalized estimating equations nonlinear regression models for categorical repeated measurements techniques for analyzing longitudinal data with non-ignorable missing observations Emphasis is given to applications of these methods, using substantial empirical illustrations, designed to help users of statistics better analyze and understand longitudinal data. Methods and Applications of Longitudinal Data Analysis equips both graduate students and professionals to confidently apply longitudinal data analysis to their particular discipline. It also provides a valuable reference source for applied statisticians, demographers and other quantitative methodologists. From novice to professional: this book starts with the introduction of basic models and ends with the description of some of the most advanced models in longitudinal data analysis Enables students to select the correct statistical methods to apply to their longitudinal data and avoid the pitfalls associated with incorrect selection Identifies the limitations of classical repeated measures models and describes newly developed techniques, along with real-world examples.

Download or read book A Practical Approach to Microarray Data Analysis written by Daniel P. Berrar and published by Springer Science & Business Media. This book was released on 2002-12-31 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past several years, DNA microarray technology has attracted tremendous interest in both the scientific community and in industry. With its ability to simultaneously measure the activity and interactions of thousands of genes, this modern technology promises unprecedented new insights into mechanisms of living systems. Currently, the primary applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery (pharmacogenomics), and toxicological research (toxicogenomics). Typical scientific tasks addressed by microarray experiments include the identification of coexpressed genes, discovery of sample or gene groups with similar expression patterns, identification of genes whose expression patterns are highly differentiating with respect to a set of discerned biological entities (e.g., tumor types), and the study of gene activity patterns under various stress conditions (e.g., chemical treatment). More recently, the discovery, modeling, and simulation of regulatory gene networks, and the mapping of expression data to metabolic pathways and chromosome locations have been added to the list of scientific tasks that are being tackled by microarray technology. Each scientific task corresponds to one or more so-called data analysis tasks. Different types of scientific questions require different sets of data analytical techniques. Broadly speaking, there are two classes of elementary data analysis tasks, predictive modeling and pattern-detection. Predictive modeling tasks are concerned with learning a classification or estimation function, whereas pattern-detection methods screen the available data for interesting, previously unknown regularities or relationships.

Download or read book Analysis of Messy Data Volume III written by George A. Milliken and published by CRC Press. This book was released on 2001-08-29 with total page 625 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysis of covariance is a very useful but often misunderstood methodology for analyzing data where important characteristics of the experimental units are measured but not included as factors in the design. Analysis of Messy Data, Volume 3: Analysis of Covariance takes the unique approach of treating the analysis of covariance problem by looking

Download or read book Covariate Information written by Debmalya Nandy and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In two major parts as described below, this dissertation presents two novelmethods for reducing the dimension of the covariate space in large supervised problems.One performs dimension reduction by means of identifying linear combinations of theoriginal covariates (we use the terms covariates, features, predictors, and explanatoryvariables interchangeably) in high-dimensional regressions. The other screens featuresas a preliminary step of analysis in ultrahigh-dimensional regressions.In Part A, building upon recent research on the applications of the DensityInformation Matrix (DIM), we develop a tool for Sufficient Dimension Reduction (SDR)in regression problems called Covariate Information Matrix (CIM). CIM exhaustivelyidentifies the Central Subspace (CS) and provides a rank ordering of the reducedcovariates in terms of their regression information. Compared to other popular SDRmethods, CIM does not require distributional assumptions on the covariates, or estimationof the mean regression function. CIM is implemented via eigen-decompositionof a matrix estimated with a previously developed efficient nonparametric densityestimation technique. We also propose a bootstrap-based diagnostic plot for estimatingthe dimension of the CS. Results of simulations and real data applications demonstratesuperior or competitive performance of CIM compared to that of some other SDRmethods. In its current formulation, CIM is applicable to scenarios where the numberof covariates (p) is high but still smaller than the number of sample units (n).In Part B, we consider a different type of scenarios, such as those generatedby contemporary high-throughput experimental and surveying techniques. Thesegive rise to ultrahigh-dimensional supervised problems with sparse signals, i.e. alimited number of observations (n), each with a very large number of covariates(p ” n), only a small share of which is truly associated with the response. In thesesettings, major concerns on computational burden, algorithmic stability and statisticalaccuracy call for substantially reducing the feature space by eliminating redundant covariates before the use of any sophisticated statistical analysis. Following thedevelopment of Sure Independence Screening (Fan and Lv, 2008) and other model andcorrelation-based feature screening methods, we propose a model-free procedurecalled Covariate Information Number - Sure Independence Screening (CIN-SIS orCIS, in short). Notably, the CIN is the univariate version of the CIM in Part A. CISuses a marginal utility built upon Fisher Information, possesses the sure screeningproperty and is applicable to any type of response. Simulations and an applicationto transcriptomic data on rats reveal CIS' comparative strengths over some popularfeature screening methods.Finally, in Part C, we discuss potential future research avenues stemming fromParts A and B. We consider some existing strategies for sparse sufficient dimensionreduction and sparse estimation of large matrices that can be adopted to estimateCIM in scenarios with p > n. Regarding CIS, we consider iterative versions of featurescreening algorithms, leading to iterative CIS. We also discuss some thresholdingstrategies to determine the cardinality of the set of features selected during screening. Inthis context, we propose a novel bootstrap-based graphical diagnostic applicable toany feature screening algorithm. Our preliminary simulation results demonstrate theeffectiveness of this diagnostic.

Download or read book Handbook of Statistical Methods and Analyses in Sports written by Jim Albert and published by CRC Press. This book was released on 2017-02-03 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook will provide both overviews of statistical methods in sports and in-depth treatment of critical problems and challenges confronting statistical research in sports. The material in the handbook will be organized by major sport (baseball, football, hockey, basketball, and soccer) followed by a section on other sports and general statistical design and analysis issues that are common to all sports. This handbook has the potential to become the standard reference for obtaining the necessary background to conduct serious statistical analyses for sports applications and to appreciate scholarly work in this expanding area.

Download or read book Foundations of Statistical Inference written by Yoel Haitovsky and published by Springer Science & Business Media. This book was released on 2003-05-22 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is a compressed survey containing recent results on statistics of stochastic processes and on identification with incomplete observations. It comprises a collection of papers presented at the Shoresh Conference 2000 on the Foundation of Statistical Inference. The papers cover the following areas with high research activity: - Identification with Incomplete Observations, Data Mining, - Bayesian Methods and Modelling, - Testing, Goodness of Fit and Randomness, - Statistics of Stationary Processes.

Download or read book Statistics of Extremes written by Jan Beirlant and published by John Wiley & Sons. This book was released on 2004-10-15 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in the statistical analysis of extreme values has flourished over the past decade: new probability models, inference and data analysis techniques have been introduced; and new application areas have been explored. Statistics of Extremes comprehensively covers a wide range of models and application areas, including risk and insurance: a major area of interest and relevance to extreme value theory. Case studies are introduced providing a good balance of theory and application of each model discussed, incorporating many illustrated examples and plots of data. The last part of the book covers some interesting advanced topics, including time series, regression, multivariate and Bayesian modelling of extremes, the use of which has huge potential.

Download or read book Machine Learning in Non Stationary Environments written by Masashi Sugiyama and published by MIT Press. This book was released on 2012-03-30 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theory, algorithms, and applications of machine learning techniques to overcome “covariate shift” non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.

Download or read book Longitudinal Categorical Data Analysis written by Brajendra C. Sutradhar and published by Springer. This book was released on 2014-10-30 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book in longitudinal categorical data analysis with parametric correlation models developed based on dynamic relationships among repeated categorical responses. This book is a natural generalization of the longitudinal binary data analysis to the multinomial data setup with more than two categories. Thus, unlike the existing books on cross-sectional categorical data analysis using log linear models, this book uses multinomial probability models both in cross-sectional and longitudinal setups. A theoretical foundation is provided for the analysis of univariate multinomial responses, by developing models systematically for the cases with no covariates as well as categorical covariates, both in cross-sectional and longitudinal setups. In the longitudinal setup, both stationary and non-stationary covariates are considered. These models have also been extended to the bivariate multinomial setup along with suitable covariates. For the inferences, the book uses the generalized quasi-likelihood as well as the exact likelihood approaches. The book is technically rigorous, and, it also presents illustrations of the statistical analysis of various real life data involving univariate multinomial responses both in cross-sectional and longitudinal setups. This book is written mainly for the graduate students and researchers in statistics and social sciences, among other applied statistics research areas. However, the rest of the book, specifically the chapters from 1 to 3, may also be used for a senior undergraduate course in statistics.

Download or read book Designing Experiments and Analyzing Data written by Scott E. Maxwell and published by Routledge. This book was released on 2017-09-11 with total page 1056 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designing Experiments and Analyzing Data: A Model Comparison Perspective (3rd edition) offers an integrative conceptual framework for understanding experimental design and data analysis. Maxwell, Delaney, and Kelley first apply fundamental principles to simple experimental designs followed by an application of the same principles to more complicated designs. Their integrative conceptual framework better prepares readers to understand the logic behind a general strategy of data analysis that is appropriate for a wide variety of designs, which allows for the introduction of more complex topics that are generally omitted from other books. Numerous pedagogical features further facilitate understanding: examples of published research demonstrate the applicability of each chapter’s content; flowcharts assist in choosing the most appropriate procedure; end-of-chapter lists of important formulas highlight key ideas and assist readers in locating the initial presentation of equations; useful programming code and tips are provided throughout the book and in associated resources available online, and extensive sets of exercises help develop a deeper understanding of the subject. Detailed solutions for some of the exercises and realistic data sets are included on the website (DesigningExperiments.com). The pedagogical approach used throughout the book enables readers to gain an overview of experimental design, from conceptualization of the research question to analysis of the data. The book and its companion website with web apps, tutorials, and detailed code are ideal for students and researchers seeking the optimal way to design their studies and analyze the resulting data.

Download or read book Bayesian Data Analysis written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-27 with total page 663 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied

Download or read book Clinical Trial Data Analysis Using R and SAS written by Ding-Geng (Din) Chen and published by CRC Press. This book was released on 2017-06-01 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Review of the First Edition "The goal of this book, as stated by the authors, is to fill the knowledge gap that exists between developed statistical methods and the applications of these methods. Overall, this book achieves the goal successfully and does a nice job. I would highly recommend it ...The example-based approach is easy to follow and makes the book a very helpful desktop reference for many biostatistics methods."—Journal of Statistical Software Clinical Trial Data Analysis Using R and SAS, Second Edition provides a thorough presentation of biostatistical analyses of clinical trial data with step-by-step implementations using R and SAS. The book’s practical, detailed approach draws on the authors’ 30 years’ experience in biostatistical research and clinical development. The authors develop step-by-step analysis code using appropriate R packages and functions and SAS PROCS, which enables readers to gain an understanding of the analysis methods and R and SAS implementation so that they can use these two popular software packages to analyze their own clinical trial data. What’s New in the Second Edition Adds SAS programs along with the R programs for clinical trial data analysis. Updates all the statistical analysis with updated R packages. Includes correlated data analysis with multivariate analysis of variance. Applies R and SAS to clinical trial data from hypertension, duodenal ulcer, beta blockers, familial andenomatous polyposis, and breast cancer trials. Covers the biostatistical aspects of various clinical trials, including treatment comparisons, time-to-event endpoints, longitudinal clinical trials, and bioequivalence trials.

Download or read book The Statistical Analysis of Multivariate Failure Time Data written by Ross L. Prentice and published by CRC Press. This book was released on 2019-05-14 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information. For example, in a context of randomized trial or cohort studies, the results go beyond that obtained by analyzing each failure time outcome in a univariate fashion. The book is addressed to researchers, practitioners, and graduate students, and can be used as a reference or as a graduate course text. Much of the literature on the analysis of censored correlated failure time data uses frailty or copula models to allow for residual dependencies among failure times, given covariates. In contrast, this book provides a detailed account of recently developed methods for the simultaneous estimation of marginal single and dual outcome hazard rate regression parameters, with emphasis on multiplicative (Cox) models. Illustrations are provided of the utility of these methods using Women’s Health Initiative randomized controlled trial data of menopausal hormones and of a low-fat dietary pattern intervention. As byproducts, these methods provide flexible semiparametric estimators of pairwise bivariate survivor functions at specified covariate histories, as well as semiparametric estimators of cross ratio and concordance functions given covariates. The presentation also describes how these innovative methods may extend to handle issues of dependent censorship, missing and mismeasured covariates, and joint modeling of failure times and covariates, setting the stage for additional theoretical and applied developments. This book extends and continues the style of the classic Statistical Analysis of Failure Time Data by Kalbfleisch and Prentice. Ross L. Prentice is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and University of Washington in Seattle, Washington. He is the recipient of COPSS Presidents and Fisher awards, the AACR Epidemiology/Prevention and Team Science awards, and is a member of the National Academy of Medicine. Shanshan Zhao is a Principal Investigator at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina.

Download or read book Analysis of Correlated Data with SAS and R written by Mohamed M. Shoukri and published by CRC Press. This book was released on 2018-04-27 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysis of Correlated Data with SAS and R: 4th edition presents an applied treatment of recently developed statistical models and methods for the analysis of hierarchical binary, count and continuous response data. It explains how to use procedures in SAS and packages in R for exploring data, fitting appropriate models, presenting programming codes and results. The book is designed for senior undergraduate and graduate students in the health sciences, epidemiology, statistics, and biostatistics as well as clinical researchers, and consulting statisticians who can apply the methods with their own data analyses. In each chapter a brief description of the foundations of statistical theory needed to understand the methods is given, thereafter the author illustrates the applicability of the techniques by providing sufficient number of examples. The last three chapters of the 4th edition contain introductory material on propensity score analysis, meta-analysis and the treatment of missing data using SAS and R. These topics were not covered in previous editions. The main reason is that there is an increasing demand by clinical researchers to have these topics covered at a reasonably understandable level of complexity. Mohamed Shoukri is principal scientist and professor of biostatistics at The National Biotechnology Center, King Faisal Specialist Hospital and Research Center and Al-Faisal University, Saudi Arabia. Professor Shoukri’s research includes analytic epidemiology, analysis of hierarchical data, and clinical biostatistics. He is an associate editor of the 3Biotech journal, a Fellow of the Royal Statistical Society and an elected member of the International Statistical Institute.