The Cox Model And Its Applications
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The Cox Model And Its Applications
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Author : Mikhail Nikulin
language : en
Publisher: Springer
Release Date : 2016-04-11
The Cox Model And Its Applications written by Mikhail Nikulin and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-11 with Mathematics categories.
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.
An Introduction To The Cox Proportional Hazards Model And Its Applications To Survival Analysis
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Author : Kristina Thompson
language : en
Publisher:
Release Date : 2014
An Introduction To The Cox Proportional Hazards Model And Its Applications To Survival Analysis written by Kristina Thompson and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
A Cox Regression Model For The Relative Mortality And Its Application To Diabetes Mllitus Survival Data
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Author : Per Kragh Andersen
language : en
Publisher:
Release Date : 1985
A Cox Regression Model For The Relative Mortality And Its Application To Diabetes Mllitus Survival Data written by Per Kragh Andersen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985 with categories.
A Cox Regression Model For The Relative Mortality And Its Application To Diabetes Mellitus Survival Data
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Author :
language : en
Publisher:
Release Date : 1985
A Cox Regression Model For The Relative Mortality And Its Application To Diabetes Mellitus Survival Data written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985 with categories.
A Cox Regression Model For The Relative Mortality And Its Application To Diabetes Mellitius Survival Data
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Author :
language : en
Publisher:
Release Date : 1985
A Cox Regression Model For The Relative Mortality And Its Application To Diabetes Mellitius Survival Data written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985 with Diabetes categories.
A New Framework For Structured Variable Selection And Its Application To Cox Models With Time Dependent Covariates
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Author : Guanbo Wang
language : en
Publisher:
Release Date : 2022
A New Framework For Structured Variable Selection And Its Application To Cox Models With Time Dependent Covariates written by Guanbo Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
"Variable selection plays an important role in statistical modeling and prediction. It can discriminate between variables that are critical to predicting the outcome and the noise variables which are irrelevant or redundant for the purpose. Thus the "best" subset of variables can be identified for prediction. In addition, in a high-dimensional setting where the sample size is less than the number of covariates, variable selection can circumvent the identifiability issue by removing noise variables, and construct a valid predictive model. In practice, researchers often have knowledge of the relationships among covariates. For instance, an interaction is obtained from the product of two or more other variables (main terms). Taking such relationships into account in the implementation of variable selection can help to identify the relevant variable subsets and thus improve the prediction accuracy. My doctoral thesis establishes a general framework for incorporating these known relationships into variable selection, broadening its utility in applications and extending it to more types of data.In the first manuscript, I propose a novel framework by first introducing the mathematical language of expressing selection rules (dependencies among the selection of variables). Then, I show that the resulting combination of permissible sets of selected variables ("selection dictionary") can be derived. I also bridge the proposed framework to existing penalized regression by offering a condition that relates to the selection dictionary: a postulated grouping structure (i.e., how to group variables in penalized regression) respecting the imposed selection rule.The second manuscript involves an application of the theory and methods developed in the first one. The aim is to identify predictors of major bleeding among hospitalized hypertensive patients using oral anticoagulants for atrial fibrillation, where adherence and drug-drug interactions are considered. I illustrate how to use the framework in practice and provide a roadmap of how to identify the grouping structure to respect some common selection rules.In the third manuscript, I focus on a versatile (in terms of respecting selection rules) penalized regression, the overlapping group Lasso, and extend it to be used in the Cox model with time-dependent covariates. Technical details are presented in a more straightforward way to reach a broader audience. Simulation studies show that the proposed method is able to handle complex selection rules with the use of the framework. Furthermore, it can better identify the variables whose coefficients are non-zero, and is associated with a lower mean squared error as compared to the non-structured variable selection method.In summary, the proposed framework highlights the importance of incorporating a priori knowledge of relationships among covariates into variable selection, advances the development of variable selection, and extends the use of existing methods"--
Biostatistical Applications In Cancer Research
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Author : Craig Beam
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14
Biostatistical Applications In Cancer Research written by Craig Beam and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Medical categories.
Biostatistics is defined as much by its application as it is by theory. This book provides an introduction to biostatistical applications in modern cancer research that is both accessible and valuable to the cancer biostatistician or to the cancer researcher, learning biostatistics. The topical areas include active areas of the application of biostatistics to modern cancer research: survival analysis, screening, diagnostics, spatial analysis and the analysis of microarray data. Biostatistics is an essential component of basic and clinical cancer research. The text, authored by distinguished figures in the field, addresses clinical issues in statistical analysis. The spectrum of topics discussed ranges from fundamental methodology to clinical and translational applications.
Fifty Years Of The Cox Model
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Author : John D. Kalbfleisch
language : en
Publisher:
Release Date : 2023
Fifty Years Of The Cox Model written by John D. Kalbfleisch and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
The Cox model is now 50 years old. The seminal paper of Sir David Cox has had an immeasurable impact on the analysis of censored survival data, with applications in many different disciplines. This work has also stimulated much additional research in diverse areas and led to important theoretical and practical advances. These include semiparametric models, nonparametric efficiency, and partial likelihood. In addition to quickly becoming the go-to method for estimating covariate effects, Cox regression has been extended to a vast number of complex data structures, to all of which the central idea of sampling from the set of individuals at risk at time can be applied. In this article, we review the Cox paper and the evolution of the ideas surrounding it. We then highlight its extensions to competing risks, with attention to models based on cause-specific hazards, and to hazards associated with the subdistribution or cumulative incidence function. We discuss their relative merits and domains of application. The analysis of recurrent events is another major topic of discussion, including an introduction to martingales and complete intensity models as well as the more practical marginal rate models. We include several worked examples to illustrate the main ideas.
Survival Analysis
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Author : Xian Liu
language : en
Publisher: John Wiley & Sons
Release Date : 2012-06-13
Survival Analysis written by Xian Liu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-06-13 with Mathematics categories.
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.
Modeling Survival Data Extending The Cox Model
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Author : Terry M. Therneau
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11
Modeling Survival Data Extending The Cox Model written by Terry M. Therneau and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-11 with Mathematics categories.
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.