Semiparametric Analysis Of An Expanded Cox Proportional Hazards Model With Time Varying Covariates PDF Download
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Author | : Wenying Zheng |
Publisher | : |
Total Pages | : 126 |
Release | : 2016 |
Genre | : |
ISBN | : |
Download Semiparametric Analysis of an Expanded Cox Proportional Hazards Model with Time-varying Covariates Book in PDF, ePub and Kindle
Time-varying covariates are often encountered in survival analysis. The Cox proportional hazards model can incorporate time-varying covariates, while the interpretation of regression parameters is less straightforward. We instead propose a complementary log-log survival model. When covariates are time-independent, the proposed model reduces to the Cox proportional hazards model; however, when they are time-varying, the proposed model provides a direct interpretation of regression parameters in the survival function. We develop semiparametric estimation procedures based on estimating equations, and establish the asymptotic properties of the estimators for the regression parameters and survival functions. In addition, we include weight functions to the estimating equations to improve efficiency. We demonstrate the proposed methods by simulation studies and application to the Mayo Clinic Primary Biliary Cirrhosis data and data from a landmark HIV randomized prevention trial.
Author | : Mikhail Nikulin |
Publisher | : Springer |
Total Pages | : 131 |
Release | : 2016-04-11 |
Genre | : Mathematics |
ISBN | : 3662493322 |
Download The Cox Model and Its Applications Book in PDF, ePub and Kindle
This book will be of interest to readers active in the fields of survival analysis, genetics, ecology, biology, demography, reliability and quality control. Since Sir David Cox’s pioneering work in 1972, the proportional hazards model has become the most important model in survival analysis. The success of the Cox model stimulated further studies in semiparametric and nonparametric theories, counting process models, study designs in epidemiology, and the development of many other regression models that could offer more flexible or more suitable approaches in data analysis. Flexible semiparametric regression models are increasingly being used to relate lifetime distributions to time-dependent explanatory variables. Throughout the book, various recent statistical models are developed in close connection with specific data from experimental studies in clinical trials or from observational studies.
Author | : Luc Duchateau |
Publisher | : Springer Science & Business Media |
Total Pages | : 329 |
Release | : 2007-10-23 |
Genre | : Mathematics |
ISBN | : 038772835X |
Download The Frailty Model Book in PDF, ePub and Kindle
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.
Author | : Xin Xin |
Publisher | : |
Total Pages | : |
Release | : 2011 |
Genre | : |
ISBN | : |
Download A STUDY OF TIES AND TIME-VARYING COVARIATES IN COX PROPORTIONAL HAZARDS MODEL. Book in PDF, ePub and Kindle
Author | : Melinda Mills |
Publisher | : SAGE |
Total Pages | : 301 |
Release | : 2011-01-19 |
Genre | : Social Science |
ISBN | : 1848601026 |
Download Introducing Survival and Event History Analysis Book in PDF, ePub and Kindle
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.
Author | : Terry M. Therneau |
Publisher | : Springer Science & Business Media |
Total Pages | : 356 |
Release | : 2013-11-11 |
Genre | : Mathematics |
ISBN | : 1475732945 |
Download Modeling Survival Data: Extending the Cox Model Book in PDF, ePub and Kindle
This book is for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyze multiple/correlated event data using marginal and random effects. The focus is on actual data examples, the analysis and interpretation of results, and computation. The book shows how these new methods can be implemented in SAS and S-Plus, including computer code, worked examples, and data sets.
Author | : David Collett |
Publisher | : |
Total Pages | : 368 |
Release | : 1993 |
Genre | : Clinical trials |
ISBN | : 9780429258374 |
Download Modelling Survival Data in Medical Research Book in PDF, ePub and Kindle
Data collected on the time to an event-such as the death of a patient in a medical study-is known as survival data. The methods for analyzing survival data can also be used to analyze data on the time to events such as the recurrence of a disease or relief from symptoms. Modelling Survival Data in Medical Research begins with an introduction to survival analysis and a description of four studies in which survival data was obtained. These and other data sets are then used to illustrate the techniques presented in the following chapters, including the Cox and Weibull proportional hazards models; accelerated failure time models; models with time-dependent variables; interval-censored survival data; model checking; and use of statistical packages. Designed for statisticians in the pharmaceutical industry and medical research institutes, and for numerate scientists and clinicians analyzing their own data sets, this book also meets the need for an intermediate text which emphasizes the application of the methodology to survival data arising from medical studies.
Author | : John D. Kalbfleisch |
Publisher | : John Wiley & Sons |
Total Pages | : 462 |
Release | : 2011-01-25 |
Genre | : Mathematics |
ISBN | : 1118031237 |
Download The Statistical Analysis of Failure Time Data Book in PDF, ePub and Kindle
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.
Author | : Jianguo Sun |
Publisher | : Springer Science & Business Media |
Total Pages | : 283 |
Release | : 2013-10-09 |
Genre | : Medical |
ISBN | : 1461487153 |
Download Statistical Analysis of Panel Count Data Book in PDF, ePub and Kindle
Panel count data occur in studies that concern recurrent events, or event history studies, when study subjects are observed only at discrete time points. By recurrent events, we mean the event that can occur or happen multiple times or repeatedly. Examples of recurrent events include disease infections, hospitalizations in medical studies, warranty claims of automobiles or system break-downs in reliability studies. In fact, many other fields yield event history data too such as demographic studies, economic studies and social sciences. For the cases where the study subjects are observed continuously, the resulting data are usually referred to as recurrent event data. This book collects and unifies statistical models and methods that have been developed for analyzing panel count data. It provides the first comprehensive coverage of the topic. The main focus is on methodology, but for the benefit of the reader, the applications of the methods to real data are also discussed along with numerical calculations. There exists a great deal of literature on the analysis of recurrent event data. This book fills the void in the literature on the analysis of panel count data. This book provides an up-to-date reference for scientists who are conducting research on the analysis of panel count data. It will also be instructional for those who need to analyze panel count data to answer substantive research questions. In addition, it can be used as a text for a graduate course in statistics or biostatistics that assumes a basic knowledge of probability and statistics.
Author | : Jun Ma |
Publisher | : CRC Press |
Total Pages | : 401 |
Release | : 2024-10-01 |
Genre | : Mathematics |
ISBN | : 1351109707 |
Download Likelihood Methods in Survival Analysis Book in PDF, ePub and Kindle
Many conventional survival analysis methods, such as the Kaplan-Meier method for survival function estimation and the partial likelihood method for Cox model regression coefficients estimation, were developed under the assumption that survival times are subject to right censoring only. However, in practice, survival time observations may include interval-censored data, especially when the exact time of the event of interest cannot be observed. When interval-censored observations are present in a survival dataset, one generally needs to consider likelihood-based methods for inference. If the survival model under consideration is fully parametric, then likelihood-based methods impose neither theoretical nor computational challenges. However, if the model is semi-parametric, there will be difficulties in both theoretical and computational aspects. Likelihood Methods in Survival Analysis: With R Examples explores these challenges and provides practical solutions. It not only covers conventional Cox models where survival times are subject to interval censoring, but also extends to more complicated models, such as stratified Cox models, extended Cox models where time-varying covariates are present, mixture cure Cox models, and Cox models with dependent right censoring. The book also discusses non-Cox models, particularly the additive hazards model and parametric log-linear models for bivariate survival times where there is dependence among competing outcomes. Features Provides a broad and accessible overview of likelihood methods in survival analysis Covers a wide range of data types and models, from the semi-parametric Cox model with interval censoring through to parametric survival models for competing risks Includes many examples using real data to illustrate the methods Includes integrated R code for implementation of the methods Supplemented by a GitHub repository with datasets and R code The book will make an ideal reference for researchers and graduate students of biostatistics, statistics, and data science, whose interest in survival analysis extend beyond applications. It offers useful and solid training to those who wish to enhance their knowledge in the methodology and computational aspects of biostatistics.