| competing risk survival analysis: 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 survival analysis: 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 survival analysis: 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 survival analysis: 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 survival analysis: 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 survival analysis: 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 survival analysis: 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 data and distributions, copulas, continuous failure, parametric likelihood inference, and non- and semi-parametric methods. There are many books covering survival analysis, but very few that cover the multivariate case in any depth. Written for a graduate-level audience in statistics/biostatistics, this book includes practical exercises and R code for the examples. The author is renowned for his clear writing style, and this book continues that trend. It is an excellent reference for graduate students and researchers looking for grounding in this burgeoning field of research. |
| competing risk survival analysis: 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 survival analysis: 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 survival analysis: 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 survival analysis: 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 survival analysis: Survival Analysis with Correlated Endpoints Takeshi Emura, Shigeyuki Matsui, Virginie Rondeau, 2019-03-25 This book introduces readers to advanced statistical methods for analyzing survival data involving correlated endpoints. In particular, it describes statistical methods for applying Cox regression to two correlated endpoints by accounting for dependence between the endpoints with the aid of copulas. The practical advantages of employing copula-based models in medical research are explained on the basis of case studies. In addition, the book focuses on clustered survival data, especially data arising from meta-analysis and multicenter analysis. Consequently, the statistical approaches presented here employ a frailty term for heterogeneity modeling. This brings the joint frailty-copula model, which incorporates a frailty term and a copula, into a statistical model. The book also discusses advanced techniques for dealing with high-dimensional gene expressions and developing personalized dynamic prediction tools under the joint frailty-copula model. To help readers apply the statistical methods to real-world data, the book provides case studies using the authors’ original R software package (freely available in CRAN). The emphasis is on clinical survival data, involving time-to-tumor progression and overall survival, collected on cancer patients. Hence, the book offers an essential reference guide for medical statisticians and provides researchers with advanced, innovative statistical tools. The book also provides a concise introduction to basic multivariate survival models. |
| competing risk survival analysis: Cancer Epidemiology Isabel dos Santos Silva, International Agency for Research on Cancer, 1999 A basic textbook addressed to medical and public health students, clinicians, health professionals, and all others seeking to understand the principles and methods used in cancer epidemiology. Written by a prominent epidemiologist and experienced teacher at the London School of Hygiene and Tropical Medicine, the text aims to help readers become competent in the use of basic epidemiological tools and capable of exercising critical judgment when assessing results reported by others. Throughout the text, a lively writing style and numerous illustrative examples, often using real research data, facilitate an easy understanding of basic concepts and methods. Information ranges from an entertaining account of the origins of epidemiology, through advice on how to overcome some of the limitations of survival analysis, to a checklist of questions to ask when considering sources of bias. Although statistical concepts and formulae are presented, the emphasis is consistently on the interpretation of the data rather than on the actual calculations. The text has 18 chapters. The first six introduce the basic principles of epidemiology and statistics. Chapters 7-13 deal in more depth with each of the study designs and interpretation of their findings. Two chapters, concerned with the problems of confounding and study size, cover more complex statistical concepts and are included for advanced study. A chapter on methodological issues in cancer prevention gives examples of epidemiology's contribution to primary prevention, screening and other activities for early detection, and tertiary prevention. The concluding chapters review the role of cancer registries and discuss practical considerations that should be taken into account in the design, planning, and conduct of any type of epidemiological research. |
| competing risk survival analysis: Survival Models and Data Analysis Regina C. Elandt-Johnson, Norman L. Johnson, 2014-11-05 Survival analysis deals with the distribution of life times, essentially the times from an initiating event such as birth or the start of a job to some terminal event such as death or pension. This book, originally published in 1980, surveys and analyzes methods that use survival measurements and concepts, and helps readers apply the appropriate method for a given situation. Four broad sections cover introductions to data, univariate survival function, multiple-failure data, and advanced topics. |
| competing risk survival analysis: Analysis of Survival Data with Dependent Censoring Takeshi Emura, Yi-Hau Chen, 2018-04-05 This book introduces readers to copula-based statistical methods for analyzing survival data involving dependent censoring. Primarily focusing on likelihood-based methods performed under copula models, it is the first book solely devoted to the problem of dependent censoring. The book demonstrates the advantages of the copula-based methods in the context of medical research, especially with regard to cancer patients’ survival data. Needless to say, the statistical methods presented here can also be applied to many other branches of science, especially in reliability, where survival analysis plays an important role. The book can be used as a textbook for graduate coursework or a short course aimed at (bio-) statisticians. To deepen readers’ understanding of copula-based approaches, the book provides an accessible introduction to basic survival analysis and explains the mathematical foundations of copula-based survival models. |
| competing risk survival analysis: Competing Risks Melania Pintilie, 2006-11-02 The need to understand, interpret and analyse competing risk data is key to many areas of science, particularly medical research. There is a real need for a book that presents an overview of methodology used in the interpretation and analysis of competing risks, with a focus on practical applications to medical problems, and incorporating modern techniques. This book fills that need by presenting the most up-to-date methodology, in a way that can be readily understood, and applied, by the practitioner. |
| competing risk survival analysis: 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 survival analysis: 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 survival analysis: 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 survival analysis: 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 survival analysis: 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 survival analysis: Survival Analysis David Machin, Yin Bun Cheung, Mahesh Parmar, 2006-03-30 Well received in its first edition, Survival Analysis: A Practical Approach is completely revised to provide an accessible and practical guide to survival analysis techniques in diverse environments. Illustrated with many authentic examples, the book introduces basic statistical concepts and methods to construct survival curves, later developing them to encompass more specialised and complex models. During the years since the first edition there have been several new topics that have come to the fore and many new applications. Parallel developments in computer software programmes, used to implement these methodologies, are relied upon throughout the text to bring it up to date. |
| competing risk survival analysis: 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 survival analysis: Event History Analysis With Stata Hans-Peter Blossfeld, Katrin Golsch, Gotz Rohwer, 2007-02-02 Event History Analysis With Stata provides an introduction to event history modeling techniques using Stata (version 9), a widely used statistical program that provides tools for data analysis. The book emphasizes the usefulness of event history models for causal analysis in the social sciences and the application of continuous-time models. T |
| competing risk survival analysis: 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 survival analysis: 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 survival analysis: Survival Analysis Using SAS Paul D. Allison, 2010-03-29 Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul D. Allison, is an accessible, data-based introduction to methods of survival analysis. Researchers who want to analyze survival data with SAS will find just what they need with this fully updated new edition that incorporates the many enhancements in SAS procedures for survival analysis in SAS 9. Although the book assumes only a minimal knowledge of SAS, more experienced users will learn new techniques of data input and manipulation. Numerous examples of SAS code and output make this an eminently practical book, ensuring that even the uninitiated become sophisticated users of survival analysis. The main topics presented include censoring, survival curves, Kaplan-Meier estimation, accelerated failure time models, Cox regression models, and discrete-time analysis. Also included are topics not usually covered in survival analysis books, such as time-dependent covariates, competing risks, and repeated events. Survival Analysis Using SAS: A Practical Guide, Second Edition, has been thoroughly updated for SAS 9, and all figures are presented using ODS Graphics. This new edition also documents major enhancements to the STRATA statement in the LIFETEST procedure; includes a section on the PROBPLOT command, which offers graphical methods to evaluate the fit of each parametric regression model; introduces the new BAYES statement for both parametric and Cox models, which allows the user to do a Bayesian analysis using MCMC methods; demonstrates the use of the counting process syntax as an alternative method for handling time-dependent covariates; contains a section on cumulative incidence functions; and describes the use of the new GLIMMIX procedure to estimate random-effects models for discrete-time data. This book is part of the SAS Press program. |
| competing risk survival analysis: Applied Survival Analysis David W. Hosmer, Jr., Stanley Lemeshow, Susanne May, 2011-09-23 THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATA—NOW IN A VALUABLE NEW EDITION Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research. This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data. Features of the Second Edition include: Expanded coverage of interactions and the covariate-adjusted survival functions The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques New discussion of variable selection with multivariable fractional polynomials Further exploration of time-varying covariates, complex with examples Additional treatment of the exponential, Weibull, and log-logistic parametric regression models Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values New examples and exercises at the end of each chapter Analyses throughout the text are performed using Stata® Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government. |
| competing risk survival analysis: Epidemiology Kenneth J. Rothman, 2012-06-21 The second edition of this essential introduction to epidemiology presents the core concepts in a unified approach that aims to cut through the fog and elucidate the fundamental concepts. |
| competing risk survival analysis: Survival Analysis David G. Kleinbaum, Mitchel Klein, 2006-01-02 An excellent introduction for all those coming to the subject for the first time. New material has been added to the second edition and the original six chapters have been modified. The previous edition sold 9500 copies world wide since its release in 1996. Based on numerous courses given by the author to students and researchers in the health sciences and is written with such readers in mind. Provides a user-friendly layout and includes numerous illustrations and exercises. 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. |
| competing risk survival analysis: 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 survival analysis: 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 survival analysis: How to Report Statistics in Medicine Thomas Allen Lang, Michelle Secic, 2006 This volume presents a comprehensive and comprehensible set of guidelines for reporting the statistical analyses and research designs and activities commonly used in biomedical research. |
| competing risk survival analysis: Contemporary Multivariate Analysis and Design of Experiments Kaitai Fang, 2005 Index. Subject index -- Author index |
| competing risk survival analysis: 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 survival analysis: Statistical Models in Epidemiology David Clayton, Michael Hills, 2013-01-17 This self-contained account of the statistical basis of epidemiology has been written for those with a basic training in biology. It is specifically intended for students enrolled for a masters degree in epidemiology, clinical epidemiology, or biostatistics. |
| competing risk survival analysis: 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 survival analysis: Survival and Event History Analysis Odd Aalen, Ornulf Borgan, Hakon Gjessing, 2008-09-16 The aim of this book is to bridge the gap between standard textbook models and a range of models where the dynamic structure of the data manifests itself fully. The common denominator of such models is stochastic processes. The authors show how counting processes, martingales, and stochastic integrals fit very nicely with censored data. Beginning with standard analyses such as Kaplan-Meier plots and Cox regression, the presentation progresses to the additive hazard model and recurrent event data. Stochastic processes are also used as natural models for individual frailty; they allow sensible interpretations of a number of surprising artifacts seen in population data. The stochastic process framework is naturally connected to causality. The authors show how dynamic path analyses can incorporate many modern causality ideas in a framework that takes the time aspect seriously. To make the material accessible to the reader, a large number of practical examples, mainly from medicine, are developed in detail. Stochastic processes are introduced in an intuitive and non-technical manner. The book is aimed at investigators who use event history methods and want a better understanding of the statistical concepts. It is suitable as a textbook for graduate courses in statistics and biostatistics. |
| competing risk survival analysis: The Customer-Funded Business John Mullins, 2014-07-21 Who needs investors? More than two generations ago, the venture capital community – VCs, business angels, incubators and others – convinced the entrepreneurial world that writing business plans and raising venture capital constituted the twin centerpieces of entrepreneurial endeavor. They did so for good reasons: the sometimes astonishing returns they've delivered to their investors and the astonishingly large companies that their ecosystem has created. But the vast majority of fast-growing companies never take any venture capital. So where does the money come from to start and grow their companies? From a much more agreeable and hospitable source, their customers. That's exactly what Michael Dell, Bill Gates and Banana Republic's Mel and Patricia Ziegler did to get their companies up and running and turn them into iconic brands. In The Customer Funded Business, best-selling author John Mullins uncovers five novel approaches that scrappy and innovative 21st century entrepreneurs working in companies large and small have ingeniously adapted from their predecessors like Dell, Gates, and the Zieglers: Matchmaker models (Airbnb) Pay-in-advance models (Threadless) Subscription models (TutorVista) Scarcity models (Vente Privee) Service-to-product models (GoViral) Through the captivating stories of these and other inspiring companies from around the world, Mullins brings to life the five models and identifies the questions that angel or other investors will – and should! – ask of entrepreneurs or corporate innovators seeking to apply them. Drawing on in-depth interviews with entrepreneurs and investors who have actually put these models to use, Mullins goes on to address the key implementation issues that characterize each of the models: when to apply them, how best to apply them, and the pitfalls to watch out for. Whether you're an aspiring entrepreneur lacking the start-up capital you need, an early-stage entrepreneur trying to get your cash-starved venture into take-off mode, an intrapreneur seeking funding within an established company, or an angel investor or mentor who supports high-potential ventures, this book offers the most sure-footed path to starting, financing, or growing your venture. John Mullins is the author of The New Business Road Test and, with Randy Komisar, the widely acclaimed Getting to Plan B. |
| competing risk survival analysis: 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 Definition & Meaning - Merriam-Webster
The meaning of COMPETING is in a state of rivalry or competition (as for position, profit, or a prize). How to use competing in a sentence.
COMPETING | English meaning - Cambridge Dictionary
COMPETING definition: 1. present participle of compete 2. to try to be more successful than someone or something else…. Learn more.
COMPETING definition and meaning | Collins English Dictionary
Competing ideas, requirements, or interests cannot all be right or satisfied at the same time. They talked about the competing theories of the origin of life. American English : competing / …
Competing - definition of competing by The Free Dictionary
Define competing. competing synonyms, competing pronunciation, competing translation, English dictionary definition of competing. intr.v. com·pet·ed , com·pet·ing , com·petes To strive …
48 Synonyms & Antonyms for COMPETING - Thesaurus.com
Find 48 different ways to say COMPETING, along with antonyms, related words, and example sentences at Thesaurus.com.
competing adjective - Definition, pictures, pronunciation and ...
Definition of competing adjective from the Oxford Advanced Learner's Dictionary. (of different ideas, interests, explanations, etc.) unable to exist or be true at the same time. There were …
Competing or Competiting – Which is Correct? - Two Minute …
Apr 15, 2025 · The correct form is competing. “Competiting” is not a valid English word. The verb “compete” follows the regular pattern in English where adding -ing to the base form creates the …
competing - WordReference.com Dictionary of English
Compete implies having a sense of rivalry and of striving to do one's best as well as to outdo another: to compete for a prize. Contend suggests opposition or disputing as well as rivalry: to …
COMPETING Synonyms: 83 Similar and Opposite Words | Merriam ...
Synonyms for COMPETING: competitive, rival, driving, hustling, hungry, eager, animated, dynamic; Antonyms of COMPETING: indifferent, disinterested, uninterested, casual, apathetic, …
What does Competing mean? - Definitions.net
Competition is a rivalry where two or more parties strive for a common goal which cannot be shared: where one's gain is the other's loss (an example of which is a zero-sum game). …
COMPETING Definition & Meaning - Merriam-Webster
The meaning of COMPETING is in a state of rivalry or competition (as for position, profit, or a prize). How to use competing in a sentence.
COMPETING | English meaning - Cambridge Dictionary
COMPETING definition: 1. present participle of compete 2. to try to be more successful than someone or something else…. Learn more.
COMPETING definition and meaning | Collins English Dictionary
Competing ideas, requirements, or interests cannot all be right or satisfied at the same time. They talked about the competing theories of the origin of life. American English : competing / …
Competing - definition of competing by The Free Dictionary
Define competing. competing synonyms, competing pronunciation, competing translation, English dictionary definition of competing. intr.v. com·pet·ed , com·pet·ing , com·petes To strive …
48 Synonyms & Antonyms for COMPETING - Thesaurus.com
Find 48 different ways to say COMPETING, along with antonyms, related words, and example sentences at Thesaurus.com.
competing adjective - Definition, pictures, pronunciation and ...
Definition of competing adjective from the Oxford Advanced Learner's Dictionary. (of different ideas, interests, explanations, etc.) unable to exist or be true at the same time. There were …
Competing or Competiting – Which is Correct? - Two Minute …
Apr 15, 2025 · The correct form is competing. “Competiting” is not a valid English word. The verb “compete” follows the regular pattern in English where adding -ing to the base form creates the …
competing - WordReference.com Dictionary of English
Compete implies having a sense of rivalry and of striving to do one's best as well as to outdo another: to compete for a prize. Contend suggests opposition or disputing as well as rivalry: to …
COMPETING Synonyms: 83 Similar and Opposite Words | Merriam ...
Synonyms for COMPETING: competitive, rival, driving, hustling, hungry, eager, animated, dynamic; Antonyms of COMPETING: indifferent, disinterested, uninterested, casual, apathetic, …
What does Competing mean? - Definitions.net
Competition is a rivalry where two or more parties strive for a common goal which cannot be shared: where one's gain is the other's loss (an example of which is a zero-sum game). …
