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| competing risk analysis in r: Competing Risks and Multistate Models with R Jan Beyersmann, Arthur Allignol, Martin Schumacher, 2011-11-18 This book covers competing risks and multistate models, sometimes summarized as event history analysis. These models generalize the analysis of time to a single event (survival analysis) to analysing the timing of distinct terminal events (competing risks) and possible intermediate events (multistate models). Both R and multistate methods are promoted with a focus on nonparametric methods. |
| competing risk analysis in r: Data Analysis with Competing Risks and Intermediate States Ronald B. Geskus, 2015-07-14 Data Analysis with Competing Risks and Intermediate States explains when and how to use models and techniques for the analysis of competing risks and intermediate states. It covers the most recent insights on estimation techniques and discusses in detail how to interpret the obtained results.After introducing example studies from the biomedical and |
| competing risk analysis in r: Multivariate Survival Analysis and Competing Risks Martin J. Crowder, 2012-04-17 Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It covers competing risks and counting processes and provides many real-world examples, exercises, and R code. The text discusses survival data, survival distributions, frailty models, parametric methods, multivariate |
| competing risk analysis in r: The Statistical Analysis of Failure Time Data John D. Kalbfleisch, Ross L. Prentice, 2011-01-25 Contains additional discussion and examples on left truncationas well as material on more general censoring and truncationpatterns. Introduces the martingale and counting process formulation swillbe in a new chapter. Develops multivariate failure time data in a separate chapterand extends the material on Markov and semi Markovformulations. Presents new examples and applications of data analysis. |
| competing risk analysis in r: R for Health Data Science Ewen Harrison, Riinu Pius, 2020-12-31 In this age of information, the manipulation, analysis, and interpretation of data have become a fundamental part of professional life; nowhere more so than in the delivery of healthcare. From the understanding of disease and the development of new treatments, to the diagnosis and management of individual patients, the use of data and technology is now an integral part of the business of healthcare. Those working in healthcare interact daily with data, often without realising it. The conversion of this avalanche of information to useful knowledge is essential for high-quality patient care. R for Health Data Science includes everything a healthcare professional needs to go from R novice to R guru. By the end of this book, you will be taking a sophisticated approach to health data science with beautiful visualisations, elegant tables, and nuanced analyses. Features Provides an introduction to the fundamentals of R for healthcare professionals Highlights the most popular statistical approaches to health data science Written to be as accessible as possible with minimal mathematics Emphasises the importance of truly understanding the underlying data through the use of plots Includes numerous examples that can be adapted for your own data Helps you create publishable documents and collaborate across teams With this book, you are in safe hands – Prof. Harrison is a clinician and Dr. Pius is a data scientist, bringing 25 years’ combined experience of using R at the coal face. This content has been taught to hundreds of individuals from a variety of backgrounds, from rank beginners to experts moving to R from other platforms. |
| competing risk analysis in r: Survival Analysis David G. Kleinbaum, Mitchel Klein, 2013-04-18 A straightforward and easy-to-follow introduction to the main concepts and techniques of the subject. It is based on numerous courses given by the author to students and researchers in the health sciences and is written with such readers in mind. A user-friendly layout includes numerous illustrations and exercises and the book is written in such a way so as to enable readers learn directly without the assistance of a classroom instructor. Throughout, there is an emphasis on presenting each new topic backed by real examples of a survival analysis investigation, followed up with thorough analyses of real data sets. Each chapter concludes with practice exercises to help readers reinforce their understanding of the concepts covered, before going on to a more comprehensive test. Answers to both are included. Readers will enjoy David Kleinbaums style of presentation, making this an excellent introduction for all those coming to the subject for the first time. |
| competing risk analysis in r: Introducing Survival and Event History Analysis Melinda Mills, 2011-01-19 This book is an accessible, practical and comprehensive guide for researchers from multiple disciplines including biomedical, epidemiology, engineering and the social sciences. Written for accessibility, this book will appeal to students and researchers who want to understand the basics of survival and event history analysis and apply these methods without getting entangled in mathematical and theoretical technicalities. Inside, readers are offered a blueprint for their entire research project from data preparation to model selection and diagnostics. Engaging, easy to read, functional and packed with enlightening examples, ‘hands-on’ exercises, conversations with key scholars and resources for both students and instructors, this text allows researchers to quickly master advanced statistical techniques. It is written from the perspective of the ‘user’, making it suitable as both a self-learning tool and graduate-level textbook. Also included are up-to-date innovations in the field, including advancements in the assessment of model fit, unobserved heterogeneity, recurrent events and multilevel event history models. Practical instructions are also included for using the statistical programs of R, STATA and SPSS, enabling readers to replicate the examples described in the text. |
| competing risk analysis in r: Joint Models for Longitudinal and Time-to-Event Data Dimitris Rizopoulos, 2012-06-22 In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author. All the R code used in the book is available at: http://jmr.r-forge.r-project.org/ |
| competing risk analysis in r: Absolute Risk Ruth M. Pfeiffer, Mitchell H. Gail, 2017-08-10 Absolute Risk: Methods and Applications in Clinical Management and Public Health provides theory and examples to demonstrate the importance of absolute risk in counseling patients, devising public health strategies, and clinical management. The book provides sufficient technical detail to allow statisticians, epidemiologists, and clinicians to build, test, and apply models of absolute risk. Features: Provides theoretical basis for modeling absolute risk, including competing risks and cause-specific and cumulative incidence regression Discusses various sampling designs for estimating absolute risk and criteria to evaluate models Provides details on statistical inference for the various sampling designs Discusses criteria for evaluating risk models and comparing risk models, including both general criteria and problem-specific expected losses in well-defined clinical and public health applications Describes many applications encompassing both disease prevention and prognosis, and ranging from counseling individual patients, to clinical decision making, to assessing the impact of risk-based public health strategies Discusses model updating, family-based designs, dynamic projections, and other topics Ruth M. Pfeiffer is a mathematical statistician and Fellow of the American Statistical Association, with interests in risk modeling, dimension reduction, and applications in epidemiology. She developed absolute risk models for breast cancer, colon cancer, melanoma, and second primary thyroid cancer following a childhood cancer diagnosis. Mitchell H. Gail developed the widely used Gail model for projecting the absolute risk of invasive breast cancer. He is a medical statistician with interests in statistical methods and applications in epidemiology and molecular medicine. He is a member of the National Academy of Medicine and former President of the American Statistical Association. Both are Senior Investigators in the Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health. |
| competing risk analysis in r: Applied Survival Analysis Using R Dirk F. Moore, 2016-05-11 Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics. |
| competing risk analysis in r: Advanced Survival Models Catherine Legrand, 2021-03-22 Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications, and in particular in medical research. During the last twenty years, several extensions of classical survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions, such as frailty models (in case of unobserved heterogeneity or clustered data), cure models (when a fraction of the population will not experience the event of interest), competing risk models (in case of different types of event), and joint survival models for a time-to-event endpoint and a longitudinal outcome. Features Presents state-of-the art approaches for different advanced survival models including frailty models, cure models, competing risk models and joint models for a longitudinal and a survival outcome Uses consistent notation throughout the book for the different techniques presented Explains in which situation each of these models should be used, and how they are linked to specific research questions Focuses on the understanding of the models, their implementation, and their interpretation, with an appropriate level of methodological development for masters students and applied statisticians Provides references to existing R packages and SAS procedure or macros, and illustrates the use of the main ones on real datasets This book is primarily aimed at applied statisticians and graduate students of statistics and biostatistics. It can also serve as an introductory reference for methodological researchers interested in the main extensions of classical survival analysis. |
| competing risk analysis in r: Classical Competing Risks Martin J. Crowder, 2001-05-11 If something can fail, it can often fail in one of several ways and sometimes in more than one way at a time. There is always some cause of failure, and almost always, more than one possible cause. In one sense, then, survival analysis is a lost cause. The methods of Competing Risks have often been neglected in the survival analysis literature. |
| competing risk analysis in r: Analysing Survival Data from Clinical Trials and Observational Studies Ettore Marubini, Maria Grazia Valsecchi, 2004-07-02 A practical guide to methods of survival analysis for medical researchers with limited statistical experience. Methods and techniques described range from descriptive and exploratory analysis to multivariate regression methods. Uses illustrative data from actual clinical trials and observational studies to describe methods of analysing and reporting results. Also reviews the features and performance of statistical software available for applying the methods of analysis discussed. |
| competing risk analysis in r: The Frailty Model Luc Duchateau, Paul Janssen, 2007-10-23 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. |
| competing risk analysis in r: Handbook of Quantile Regression Roger Koenker, Victor Chernozhukov, Xuming He, Limin Peng, 2017-10-12 Quantile regression constitutes an ensemble of statistical techniques intended to estimate and draw inferences about conditional quantile functions. Median regression, as introduced in the 18th century by Boscovich and Laplace, is a special case. In contrast to conventional mean regression that minimizes sums of squared residuals, median regression minimizes sums of absolute residuals; quantile regression simply replaces symmetric absolute loss by asymmetric linear loss. Since its introduction in the 1970's by Koenker and Bassett, quantile regression has been gradually extended to a wide variety of data analytic settings including time series, survival analysis, and longitudinal data. By focusing attention on local slices of the conditional distribution of response variables it is capable of providing a more complete, more nuanced view of heterogeneous covariate effects. Applications of quantile regression can now be found throughout the sciences, including astrophysics, chemistry, ecology, economics, finance, genomics, medicine, and meteorology. Software for quantile regression is now widely available in all the major statistical computing environments. The objective of this volume is to provide a comprehensive review of recent developments of quantile regression methodology illustrating its applicability in a wide range of scientific settings. The intended audience of the volume is researchers and graduate students across a diverse set of disciplines. |
| competing risk analysis in r: Handbook of Survival Analysis John P. Klein, Hans C. van Houwelingen, Joseph G. Ibrahim, Thomas H. Scheike, 2016-04-19 Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides: An introduction to various areas in survival analysis for graduate students and novices A reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysis A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians |
| competing risk analysis in r: Survival Analysis Using S Mara Tableman, Jong Sung Kim, 2003-07-28 Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics. The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s). In a chapter written by Stephen Portnoy, censored regression quantiles - a new nonparametric regression methodology (2003) - is developed to identify important forms of population heterogeneity and to detect departures from traditional Cox models. By generalizing the Kaplan-Meier estimator to regression models for conditional quantiles, this methods provides a valuable complement to traditional Cox proportional hazards approaches. |
| competing risk analysis in r: Probabilistic Risk Analysis Tim Bedford, Roger Cooke, 2001-04-30 Probabilistic risk analysis aims to quantify the risk caused by high technology installations. Increasingly, such analyses are being applied to a wider class of systems in which problems such as lack of data, complexity of the systems, uncertainty about consequences, make a classical statistical analysis difficult or impossible. The authors discuss the fundamental notion of uncertainty, its relationship with probability, and the limits to the quantification of uncertainty. Drawing on extensive experience in the theory and applications of risk analysis, the authors focus on the conceptual and mathematical foundations underlying the quantification, interpretation and management of risk. They cover standard topics as well as important new subjects such as the use of expert judgement and uncertainty propagation. The relationship of risk analysis with decision making is highlighted in chapters on influence diagrams and decision theory. Finally, the difficulties of choosing metrics to quantify risk, and current regulatory frameworks are discussed. |
| competing risk analysis in r: Dynamic Regression Models for Survival Data Torben Martinussen, Thomas H. Scheike, 2007-11-24 This book studies and applies modern flexible regression models for survival data with a special focus on extensions of the Cox model and alternative models with the aim of describing time-varying effects of explanatory variables. Use of the suggested models and methods is illustrated on real data examples, using the R-package timereg developed by the authors, which is applied throughout the book with worked examples for the data sets. |
| competing risk analysis in r: The R Book Michael J. Crawley, 2007-06-13 The high-level language of R is recognized as one of the mostpowerful and flexible statistical software environments, and israpidly becoming the standard setting for quantitative analysis,statistics and graphics. R provides free access to unrivalledcoverage and cutting-edge applications, enabling the user to applynumerous statistical methods ranging from simple regression to timeseries or multivariate analysis. Building on the success of the author’s bestsellingStatistics: An Introduction using R, The R Book ispacked with worked examples, providing an all inclusive guide to R,ideal for novice and more accomplished users alike. The bookassumes no background in statistics or computing and introduces theadvantages of the R environment, detailing its applications in awide range of disciplines. Provides the first comprehensive reference manual for the Rlanguage, including practical guidance and full coverage of thegraphics facilities. Introduces all the statistical models covered by R, beginningwith simple classical tests such as chi-square and t-test. Proceeds to examine more advance methods, from regression andanalysis of variance, through to generalized linear models,generalized mixed models, time series, spatial statistics,multivariate statistics and much more. The R Book is aimed at undergraduates, postgraduates andprofessionals in science, engineering and medicine. It is alsoideal for students and professionals in statistics, economics,geography and the social sciences. |
| competing risk analysis in r: R for SAS and SPSS Users Robert A. Muenchen, 2011-08-27 R is a powerful and free software system for data analysis and graphics, with over 5,000 add-on packages available. This book introduces R using SAS and SPSS terms with which you are already familiar. It demonstrates which of the add-on packages are most like SAS and SPSS and compares them to R's built-in functions. It steps through over 30 programs written in all three packages, comparing and contrasting the packages' differing approaches. The programs and practice datasets are available for download. The glossary defines over 50 R terms using SAS/SPSS jargon and again using R jargon. The table of contents and the index allow you to find equivalent R functions by looking up both SAS statements and SPSS commands. When finished, you will be able to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses. This new edition has updated programming, an expanded index, and even more statistical methods covered in over 25 new sections. |
| competing risk analysis in r: Decision Modelling for Health Economic Evaluation Andrew H. Briggs, Karl Claxton, Mark J. Sculpher, 2006 In financially constrained health systems across the world, increasing emphasis is being placed on the ability to demonstrate that health care interventions are not only effective, but also cost-effective. This book deals with decision modelling techniques that can be used to estimate the value for money of various interventions including medical devices, surgical procedures, diagnostic technologies, and pharmaceuticals. Particular emphasis is placed on the importance of the appropriate representation of uncertainty in the evaluative process and the implication this uncertainty has for decision making and the need for future research. This highly practical guide takes the reader through the key principles and approaches of modelling techniques. It begins with the basics of constructing different forms of the model, the population of the model with input parameter estimates, analysis of the results, and progression to the holistic view of models as a valuable tool for informing future research exercises. Case studies and exercises are supported with online templates and solutions. This book will help analysts understand the contribution of decision-analytic modelling to the evaluation of health care programmes. ABOUT THE SERIES: Economic evaluation of health interventions is a growing specialist field, and this series of practical handbooks will tackle, in-depth, topics superficially addressed in more general health economics books. Each volume will include illustrative material, case histories and worked examples to encourage the reader to apply the methods discussed, with supporting material provided online. This series is aimed at health economists in academia, the pharmaceutical industry and the health sector, those on advanced health economics courses, and health researchers in associated fields. |
| competing risk analysis in r: Modelling Mortality with Actuarial Applications Angus S. Macdonald, Stephen J. Richards, Iain D. Currie, 2018-05-03 Modern mortality modelling for actuaries and actuarial students, with example R code, to unlock the potential of individual data. |
| competing risk analysis in r: Statistical Models Based on Counting Processes Per K. Andersen, Ornulf Borgan, Richard D. Gill, Niels Keiding, 2012-12-06 Modern survival analysis and more general event history analysis may be effectively handled within the mathematical framework of counting processes. This book presents this theory, which has been the subject of intense research activity over the past 15 years. The exposition of the theory is integrated with careful presentation of many practical examples, drawn almost exclusively from the authors'own experience, with detailed numerical and graphical illustrations. Although Statistical Models Based on Counting Processes may be viewed as a research monograph for mathematical statisticians and biostatisticians, almost all the methods are given in concrete detail for use in practice by other mathematically oriented researchers studying event histories (demographers, econometricians, epidemiologists, actuarial mathematicians, reliability engineers and biologists). Much of the material has so far only been available in the journal literature (if at all), and so a wide variety of researchers will find this an invaluable survey of the subject. |
| competing risk analysis in r: Regression Modeling Strategies Frank E. Harrell, 2013-03-09 Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with too many variables to analyze and not enough observations, and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve safe data mining. |
| competing risk analysis in r: Event History Analysis Paul David Allison, 1984-11 Drawing on recent event history analytical methods from biostatistics, engineering, and sociology, this clear and comprehensive monograph explains how longitudinal data can be used to study the causes of deaths, crimes, wars, and many other human events. Allison shows why ordinary multiple regression is not suited to analyze event history data, and demonstrates how innovative regression - like methods can overcome this problem. He then discusses the particular new methods that social scientists should find useful. |
| competing risk analysis in r: Statistical Methods for Survival Trial Design Jianrong Wu, 2018-06-14 Statistical Methods for Survival Trial Design: With Applications to Cancer Clinical Trials Using R provides a thorough presentation of the principles of designing and monitoring cancer clinical trials in which time-to-event is the primary endpoint. Traditional cancer trial designs with time-to-event endpoints are often limited to the exponential model or proportional hazards model. In practice, however, those model assumptions may not be satisfied for long-term survival trials. This book is the first to cover comprehensively the many newly developed methodologies for survival trial design, including trial design under the Weibull survival models; extensions of the sample size calculations under the proportional hazard models; and trial design under mixture cure models, complex survival models, Cox regression models, and competing-risk models. A general sequential procedure based on the sequential conditional probability ratio test is also implemented for survival trial monitoring. All methodologies are presented with sufficient detail for interested researchers or graduate students. |
| competing risk analysis in r: Survival Analysis John P. Klein, Melvin L. Moeschberger, 2013-06-29 Making complex methods more accessible to applied researchers without an advanced mathematical background, the authors present the essence of new techniques available, as well as classical techniques, 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, while technical details of the derivation of the techniques are sketched in the technical notes. This book will thus be useful for investigators who need to analyse censored or truncated life time data, and as a textbook for a graduate course in survival analysis, the only prerequisite being a standard course in statistical methodology. |
| competing risk analysis in r: Artificial Neural Networks and Machine Learning – ICANN 2018 Věra Kůrková, Yannis Manolopoulos, Barbara Hammer, Lazaros Iliadis, Ilias Maglogiannis, 2018-10-02 This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning. |
| competing risk analysis in r: Survival Analysis Xian Liu, 2012-06-13 Survival analysis concerns sequential occurrences of events governed by probabilistic laws. Recent decades have witnessed many applications of survival analysis in various disciplines. This book introduces both classic survival models and theories along with newly developed techniques. Readers will learn how to perform analysis of survival data by following numerous empirical illustrations in SAS. Survival Analysis: Models and Applications: Presents basic techniques before leading onto some of the most advanced topics in survival analysis. Assumes only a minimal knowledge of SAS whilst enabling more experienced users to learn new techniques of data input and manipulation. Provides numerous examples of SAS code to illustrate each of the methods, along with step-by-step instructions to perform each technique. Highlights the strengths and limitations of each technique covered. Covering a wide scope of survival techniques and methods, from the introductory to the advanced, this book can be used as a useful reference book for planners, researchers, and professors who are working in settings involving various lifetime events. Scientists interested in survival analysis should find it a useful guidebook for the incorporation of survival data and methods into their projects. |
| competing risk analysis in r: Counting Processes and Survival Analysis Thomas R. Fleming, David P. Harrington, 2011-09-20 The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. The book is a valuable completion of the literature in this field. It is written in an ambitious mathematical style and can be recommended to statisticians as well as biostatisticians. -Biometrische Zeitschrift Not many books manage to combine convincingly topics from probability theory over mathematical statistics to applied statistics. This is one of them. The book has other strong points to recommend it: it is written with meticulous care, in a lucid style, general results being illustrated by examples from statistical theory and practice, and a bunch of exercises serve to further elucidate and elaborate on the text. -Mathematical Reviews This book gives a thorough introduction to martingale and counting process methods in survival analysis thereby filling a gap in the literature. -Zentralblatt für Mathematik und ihre Grenzgebiete/Mathematics Abstracts The authors have performed a valuable service to researchers in providing this material in [a] self-contained and accessible form. . . This text [is] essential reading for the probabilist or mathematical statistician working in the area of survival analysis. -Short Book Reviews, International Statistical Institute Counting Processes and Survival Analysis explores the martingale approach to the statistical analysis of counting processes, with an emphasis on the application of those methods to censored failure time data. This approach has proven remarkably successful in yielding results about statistical methods for many problems arising in censored data. A thorough treatment of the calculus of martingales as well as the most important applications of these methods to censored data is offered. Additionally, the book examines classical problems in asymptotic distribution theory for counting process methods and newer methods for graphical analysis and diagnostics of censored data. Exercises are included to provide practice in applying martingale methods and insight into the calculus itself. |
| competing risk analysis in r: Logistics Management and Strategy Alan Harrison, Heather Skipworth, Remko I. van Hoek, James Aitken, 2019 |
| competing risk analysis in r: A Visual Guide to Stata Graphics, Second Edition Michael N. Mitchell, 2008-06-04 The Power of Stata Graphics at Your Fingertips Whether you are new to Stata graphics or a seasoned veteran, this book teaches you how to use Stata to make high-quality graphs that stand out and enhance statistical results. With over 900 illustrated examples and quick-reference tabs, it offers a guide to creating and customizing graphs for any type of statistical data using either Stata commands or the Graph Editor. The author displays each graph example in full color with simple and clear instructions. He shows how to produce various types of graph elements, including marker symbols, lines, legends, captions, titles, axis labels, and grid lines. Reflecting the new graphics features of Stata, this thoroughly updated and expanded edition contains a new chapter that explains how to exploit the power of the new Graph Editor. This edition also includes additional examples and illustrates nearly every example with the Graph Editor. |
| competing risk analysis in r: Interval-Censored Time-to-Event Data Ding-Geng (Din) Chen, Jianguo Sun, Karl E. Peace, 2012-07-19 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. |
| competing risk analysis in r: Handbook of Statistical Data Editing and Imputation Ton de Waal, Jeroen Pannekoek, Sander Scholtus, 2011-03-04 A practical, one-stop reference on the theory and applications of statistical data editing and imputation techniques Collected survey data are vulnerable to error. In particular, the data collection stage is a potential source of errors and missing values. As a result, the important role of statistical data editing, and the amount of resources involved, has motivated considerable research efforts to enhance the efficiency and effectiveness of this process. Handbook of Statistical Data Editing and Imputation equips readers with the essential statistical procedures for detecting and correcting inconsistencies and filling in missing values with estimates. The authors supply an easily accessible treatment of the existing methodology in this field, featuring an overview of common errors encountered in practice and techniques for resolving these issues. The book begins with an overview of methods and strategies for statistical data editing and imputation. Subsequent chapters provide detailed treatment of the central theoretical methods and modern applications, with topics of coverage including: Localization of errors in continuous data, with an outline of selective editing strategies, automatic editing for systematic and random errors, and other relevant state-of-the-art methods Extensions of automatic editing to categorical data and integer data The basic framework for imputation, with a breakdown of key methods and models and a comparison of imputation with the weighting approach to correct for missing values More advanced imputation methods, including imputation under edit restraints Throughout the book, the treatment of each topic is presented in a uniform fashion. Following an introduction, each chapter presents the key theories and formulas underlying the topic and then illustrates common applications. The discussion concludes with a summary of the main concepts and a real-world example that incorporates realistic data along with professional insight into common challenges and best practices. Handbook of Statistical Data Editing and Imputation is an essential reference for survey researchers working in the fields of business, economics, government, and the social sciences who gather, analyze, and draw results from data. It is also a suitable supplement for courses on survey methods at the upper-undergraduate and graduate levels. |
| competing risk analysis in r: Flexible Parametric Survival Analysis Using Stata Patrick Royston, Paul C. Lambert, 2011-08-04 Through real-world case studies, this book shows how to use Stata to estimate a class of flexible parametric survival models. It discusses the modeling of time-dependent and continuous covariates and looks at how relative survival can be used to measure mortality associated with a particular disease when the cause of death has not been recorded. The book describes simple quantification of differences between any two covariate patterns through calculation of time-dependent hazard ratios, hazard differences, and survival differences. |
| competing risk analysis in r: Event History Analysis Kazuo Yamaguchi, 1991-07-18 In a manner similar to many other titles within the Applied Social Research Methods Series, this 182-page book thoroughly covers many of the specific methodological hurdles encountered in implementing event history analysis (EHA). The Applied Social Research Methods Series' ... is the result of careful subject selection. ... Consistent with the practical orientation of the book, each of the application sections provides useful insights into data structure problems and programming notes. ... Kazuo Yamaguchi's insightful review of problems in structuring EHA models is useful for those contemplating life-course research. ... We strongly recommend its inclusion in the libraries of marketing researchers and its inclusion on suggested reading lists of graduate research method seminars.--Journal of Marketing Research This book, which is part of Sage Publications' Applied Social Research Methods Series, is a practical guide for those interested in using event history analysis. ... The book's strength is that it is well written and easy to understand. Even those with limited statistical backgrounds can follow the discussion and the systematic progression from the simpler to the more complex models (although the author provides ample references for those wanting a more rigorous discussion). ... Upon finishing the book, I found myself wondering about specific accounting questions that might be addressed using event history analysis. There are many, and in fact, most issues can be recast in an events framework. ... In sum, I recommend this book to anyone wanting to use event history analysis whether to apply to new research questions or to provide a fresh look at old questions. --The Accounting Review A significant introduction to the event-history literature that provides the background to implement this difficult methodology successfully and that can be supplemented with other, more advanced texts. It will undoubtedly become a prized text among students and a valuable reference for the research community. --Contemporary Sociology As a research tool event history analysis has recently become a key technique for researchers, professionals and students in a wide range of disciplines. However, despite this increasing interest, few resources exist which clearly examine this technique. Now, Event History Analysis provides a systematic introduction to models, methods and applications of event history analysis. Kazuo Yamaguchi emphasizes hands on information, including the use and misuse of samples, models, and covariates in applications, the structural arrangement of input data, the specification of various models in such computer programs as SAS-LOGIST and SPSS-LOGLINEAR, and the interpretation of parameters estimated from models. This timely book also offers such significant topics as missing data, hazard rate, Cox's partial likelihood model, survivor function, and discrete-time logit models for both one-way and two-way transitions. Event History Analysis is essential for researchers, professionals and students of public health, sociology, labor economics, political science, and organization studies.-Provided by published. |
| competing risk analysis in r: Doing Meta-Analysis with R Mathias Harrer, Pim Cuijpers, Toshi A. Furukawa, David D. Ebert, 2021-09-15 Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book |
| competing risk analysis in r: An Introduction to Survival Analysis Using Stata, Second Edition Mario Cleves, 2008-05-15 [This book] provides new researchers with the foundation for understanding the various approaches for analyzing time-to-event data. This book serves not only as a tutorial for those wishing to learn survival analysis but as a ... reference for experienced researchers ...--Book jacket. |
| competing risk analysis in r: Dynamic Prediction in Clinical Survival Analysis Hans van Houwelingen, Hein Putter, 2011-11-09 There is a huge amount of literature on statistical models for the prediction of survival after diagnosis of a wide range of diseases like cancer, cardiovascular disease, and chronic kidney disease. Current practice is to use prediction models based on the Cox proportional hazards model and to present those as static models for remaining lifetime a |
Competing Risks with R - utstat.toronto.edu
Here is a simple model for competing risks. Time is always discrete in practice. Roll a die. Time to event is the number of rolls. Dependence on x variables through an extension of logistic …
Competing risk analysis using R: an easy guide for clinicians
tests, clustering, smoothing and survival analysis. R als. provides additional packages for specific purposes. We show how to perform a competing risk analysis in R by an example...
Regression modeling of competing risk using R: an in depth
Scrucca et al.4 showed how to perform a competing risk analysis in R using an add-on package called cmprsk. They also discussed installation of the R software and the cmprsk package.
Survival Modeling of Competing Risk Using R: An …
simulated data on how to execute competing risk regression analysis with R. Keywords Competing Risks, Cumulative Incidence, Cause-Specific Hazards, Sub-Distribution Hazard, Model Selection …
A TUTORIAL ON ACCOUNTING FOR COMPETING RISKS IN …
Feb 8, 2016 · Use the Fine-Gray subdistribution hazard model when the focus is on estimating incidence or predicting prognosis in the presence of competing risks. Use the cause-specific …
Analyzing Competing Risk Data Using the R timereg Package
In this paper we describe exible competing risks regression models using the comp.risk() function available in the timereg package for R based on Scheike et al. (2008). Regression models are …
Competing Risks Survival Analysis - UZH
Several R packages exist for the analysis of competing risks. The package survival (Therneau T (2015). A Package for Survival Analysis in S. version 2.38) can be used for survival analysis in …
SemiCompRisks: An R Package for the Analysis of ... - The R …
In this paper, we present the R package SemiCompRisks that provides functions to perform the analysis of independent/clustered semi-competing risks data under the illness-death multi-state …
CFC: Cause-Specific Framework for Competing-Risk Analysis
Preparing a data frame and formulas for cause-specific competing-risk survival analysis. It expands the multi-state status column into a series of binary columns by treating an event for a cause as
riskRegression: Predicting the Risk of an Event using ... - The R …
We present computationally fast and memory optimized C++ functions with an R inter-face for predicting the covariate specific absolute risks, their confidence intervals, and their confidence …
tidycmprsk: Competing Risks Estimation - The Comprehensive …
Title Competing Risks Estimation Version 1.1.0 Description Provides an intuitive interface for working with the competing risk endpoints. The package wraps the 'cmprsk' package, and …
mstate: An R Package for the Analysis of Competing Risks and …
The package covers all steps of the analysis of multi-state models, from model building and data preparation to estimation and graphical representation of the results. It can be applied to non- …
Survival analysis in the presence of competing risks - amegroups
Competing risks arise in clinical research when there are more than one possible outcome during follow up for survival data, and the occurrence of an outcome of interest can be precluded by …
crrSC: Competing Risks Regression for Stratified and Clustered …
crrc Competing Risks Regression for Clustered Data Description Regression modeling of subdistribution hazards for clustered right censored data. Failure times within the same cluster …
JMcmprsk: An R Package for Joint Modelling of ... - The R Journal
In the current version of JMcmprsk, we have implemented two joint models for competing risk data, namely joint modeling with continuous longitudinal outcomes (Elashoff et al., 2008), and joint …
riskRegression: Risk Regression Models and Prediction Scores …
A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under …
cmprskcoxmsm: Use IPW to Estimate Treatment Effect under …
Description Uses inverse probability weighting methods to estimate treatment effect un-der marginal structure model for the cause-specific hazard of competing risk events. Esti-mates also …
Multi-state models and competing risks - The …
The lower left diagram depicts a classic competing risk analysis, where all subjects start on the left and each subject can make a single transition to one of 3 terminal states. The bottom right …
Competing Risks with R - utstat.toronto.edu
Here is a simple model for competing risks. Time is always discrete in practice. Roll a die. Time to event is the number of rolls. Dependence on x variables through an extension of logistic …
Competing risk analysis using R: an easy guide for clinicians
tests, clustering, smoothing and survival analysis. R als. provides additional packages for specific purposes. We show how to perform a competing risk analysis in R by an example...
A Fast and Scalable Implementation Method for …
We have developed an R package, fastcmprsk, that uses a novel forward-backward scan algorithm to significantly reduce the computational complexity for parameter estimation by …
Regression modeling of competing risk using R: an in depth …
Scrucca et al.4 showed how to perform a competing risk analysis in R using an add-on package called cmprsk. They also discussed installation of the R software and the cmprsk package.
Survival Modeling of Competing Risk Using R: An …
simulated data on how to execute competing risk regression analysis with R. Keywords Competing Risks, Cumulative Incidence, Cause-Specific Hazards, Sub-Distribution Hazard, …
A TUTORIAL ON ACCOUNTING FOR COMPETING RISKS IN …
Feb 8, 2016 · Use the Fine-Gray subdistribution hazard model when the focus is on estimating incidence or predicting prognosis in the presence of competing risks. Use the cause-specific …
cmprsk: Subdistribution Analysis of Competing Risks
Fits the ’proportional subdistribution hazards’ regression model described in Fine and Gray (1999). This model directly assesses the effect of covariates on the subdistribution of a …
Analyzing Competing Risk Data Using the R timereg Package
In this paper we describe exible competing risks regression models using the comp.risk() function available in the timereg package for R based on Scheike et al. (2008). Regression models are …
Competing Risks Survival Analysis - UZH
Several R packages exist for the analysis of competing risks. The package survival (Therneau T (2015). A Package for Survival Analysis in S. version 2.38) can be used for survival analysis in …
SemiCompRisks: An R Package for the Analysis of ... - The R …
In this paper, we present the R package SemiCompRisks that provides functions to perform the analysis of independent/clustered semi-competing risks data under the illness-death multi …
CFC: Cause-Specific Framework for Competing-Risk Analysis
Preparing a data frame and formulas for cause-specific competing-risk survival analysis. It expands the multi-state status column into a series of binary columns by treating an event for …
riskRegression: Predicting the Risk of an Event using ... - The …
We present computationally fast and memory optimized C++ functions with an R inter-face for predicting the covariate specific absolute risks, their confidence intervals, and their confidence …
tidycmprsk: Competing Risks Estimation - The …
Title Competing Risks Estimation Version 1.1.0 Description Provides an intuitive interface for working with the competing risk endpoints. The package wraps the 'cmprsk' package, and …
mstate: An R Package for the Analysis of Competing Risks …
The package covers all steps of the analysis of multi-state models, from model building and data preparation to estimation and graphical representation of the results. It can be applied to non- …
Survival analysis in the presence of competing risks
Competing risks arise in clinical research when there are more than one possible outcome during follow up for survival data, and the occurrence of an outcome of interest can be precluded by …
crrSC: Competing Risks Regression for Stratified and …
crrc Competing Risks Regression for Clustered Data Description Regression modeling of subdistribution hazards for clustered right censored data. Failure times within the same cluster …
JMcmprsk: An R Package for Joint Modelling of ... - The R …
In the current version of JMcmprsk, we have implemented two joint models for competing risk data, namely joint modeling with continuous longitudinal outcomes (Elashoff et al., 2008), and …
riskRegression: Risk Regression Models and Prediction Scores …
A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under …
cmprskcoxmsm: Use IPW to Estimate Treatment Effect under …
Description Uses inverse probability weighting methods to estimate treatment effect un-der marginal structure model for the cause-specific hazard of competing risk events. Esti-mates …
