Applied Survival Analysis Using R

2016-05-11
 |  Medical
Applied Survival Analysis Using R

Author: Dirk F. Moore

Publisher: Springer

ISBN: 9783319312453

Category: Medical

Page: 226

View: 722

Get eBOOK →
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.

Applied Survival Analysis

2011-09-23
 |  Mathematics
Applied Survival Analysis

Author: David W. Hosmer, Jr.

Publisher: John Wiley & Sons

ISBN: 9781118211588

Category: Mathematics

Page: 416

View: 609

Get eBOOK →
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.

Survival Analysis by Example

2016-11-08
 | 
Survival Analysis by Example

Author: Faye Anderson

Publisher: Createspace Independent Publishing Platform

ISBN: 1540314359

Category:

Page: 40

View: 371

Get eBOOK →
This is an applied handbook on survival analysis (also known as reliability or duration analysis) with annotated examples using S-Plus or R. This is the first book ever explaining survival analysis by example and is intended for users at all levels. The examples can easily be replicated using other software. Key topics include exploratory analyses, parametric, non-parametric and semi-parametric models, and model selection.

Reliability and Survival Analysis

2019-08-09
 |  Medical
Reliability and Survival Analysis

Author: Md. Rezaul Karim

Publisher: Springer

ISBN: 9789811397769

Category: Medical

Page: 252

View: 662

Get eBOOK →
This book presents and standardizes statistical models and methods that can be directly applied to both reliability and survival analysis. These two types of analysis are widely used in many fields, including engineering, management, medicine, actuarial science, the environmental sciences, and the life sciences. Though there are a number of books on reliability analysis and a handful on survival analysis, there are virtually no books on both topics and their overlapping concepts. Offering an essential textbook, this book will benefit students, researchers, and practitioners in reliability and survival analysis, reliability engineering, biostatistics, and the biomedical sciences.

Applied Survival Analysis

1997-10-28
 |  Mathematics
Applied Survival Analysis

Author: Chap T. Le

Publisher: Wiley-Interscience

ISBN: 0471170852

Category: Mathematics

Page: 0

View: 651

Get eBOOK →
This concise, application-oriented text is designed to meet the needs of practitioners and students in applied fields in its coverage of major, updated methods in the analysis of survival data. Includes analysis of standardized mortality ratios, methods for proving attenuation of healthy worker effects, ordinal risk factors and other new areas of research. Timely and diverse case studies are presented, plus a complete data set on ESRD patients on hemodialysis. Moderate level of mathematics required.

Biostatistics explored through R software: An overview

2021-09-10
 |  Medical
Biostatistics explored through R software: An overview

Author: Vinaitheerthan Renganathan

Publisher: Vinaitheerthan Renganathan

ISBN: 9789354936586

Category: Medical

Page: 107

View: 199

Get eBOOK →
The term Statistics and Data is now familiar to most of us even though we are related to different fields such as Clinical, Biology, Business & Management, Engineering and Physical Sciences. Statistical methods are being applied in different fields including the emerging fields such as Machine Learning, Natural Language Processing and Artificial Intelligence because of the fact that each field deals with data in one form or the other. Statistical methods help decision makers to arrive at evidence based decisions by collecting, analyzing and interpreting the data. Biostatistics is a branch of statistics which is applied in the biomedical domain and it includes clinical and biological fields. Biostatistical methods include descriptive, inferential and Statistical modeling tools. Descriptive statistical tools help us to draw information and summary about the data in terms of measures of central tendency, measures of dispersion, measure of skewness and underlying probability distributions. Inferential Statistical tools help us to estimate, test the hypothesis and draw inferences about the parameters of the data. Statistical modeling tools help us to study and estimate the relationship between the variables. The book starts with the introduction of concepts in biostatistics and moves on to explore descriptive and inferential statistical tools using R Open Source Software. It then explores statistical modeling tools such as regression analysis and survival analysis. The book also covers the role of Biostatistics in the clinical trials process. Clinical trials are scientific experiments which are conducted in different phases. It involves testing of drugs and clinical procedures for treating different diseases and conditions among a sample of participants before rolling out in the market. Biostatistics tools help clinical trial researchers in deciding about the study design, sample size and analytical methods. The data set used in the book can be downloaded from Author’s Website.

Methods and Biostatistics in Oncology

2018-04-16
 |  Medical
Methods and Biostatistics in Oncology

Author: Raphael. L.C Araújo

Publisher: Springer

ISBN: 9783319713243

Category: Medical

Page: 348

View: 994

Get eBOOK →
This book introduces and discusses the most important aspects of clinical research methods and biostatistics for oncologists, pursuing a tailor-made and practical approach. Evidence-based medicine (EBM) has been in vogue in the last few decades, particularly in rapidly advancing fields such as oncology. This approach has been used to support decision-making processes worldwide, sparking new clinical research and guidelines on clinical and surgical oncology. Clinical oncology research has many peculiarities, including specific study endpoints, a special focus on survival analyses, and a unique perspective on EBM. However, during medical studies and in general practice, these topics are barely taught. Moreover, even when EBM and clinical cancer research are discussed, they are presented in a theoretical fashion, mostly focused on formulas and numbers, rather than on clinical application for a proper literature appraisal. Addressing that gap, this book discusses more practical aspects of clinical research and biostatistics in oncology, instead of relying only on mathematical formulas and theoretical considerations. Methods and Biostatistics in Oncology will help readers develop the skills they need to understand the use of research on everyday oncology clinical practice for study design and interpretation, as well to demystify the use of EBM in oncology.

Bayesian Cost-Effectiveness Analysis with the R package BCEA

2017-05-25
 |  Medical
Bayesian Cost-Effectiveness Analysis with the R package BCEA

Author: Gianluca Baio

Publisher: Springer

ISBN: 9783319557182

Category: Medical

Page: 168

View: 202

Get eBOOK →
The book provides a description of the process of health economic evaluation and modelling for cost-effectiveness analysis, particularly from the perspective of a Bayesian statistical approach. Some relevant theory and introductory concepts are presented using practical examples and two running case studies. The book also describes in detail how to perform health economic evaluations using the R package BCEA (Bayesian Cost-Effectiveness Analysis). BCEA can be used to post-process the results of a Bayesian cost-effectiveness model and perform advanced analyses producing standardised and highly customisable outputs. It presents all the features of the package, including its many functions and their practical application, as well as its user-friendly web interface. The book is a valuable resource for statisticians and practitioners working in the field of health economics wanting to simplify and standardise their workflow, for example in the preparation of dossiers in support of marketing authorisation, or academic and scientific publications.

Heart Rate Variability Analysis with the R package RHRV

2017-09-18
 |  Medical
Heart Rate Variability Analysis with the R package RHRV

Author: Constantino Antonio García Martínez

Publisher: Springer

ISBN: 9783319653556

Category: Medical

Page: 157

View: 325

Get eBOOK →
This book introduces readers to the basic concepts of Heart Rate Variability (HRV) and its most important analysis algorithms using a hands-on approach based on the open-source RHRV software. HRV refers to the variation over time of the intervals between consecutive heartbeats. Despite its apparent simplicity, HRV is one of the most important markers of the autonomic nervous system activity and it has been recognized as a useful predictor of several pathologies. The book discusses all the basic HRV topics, including the physiological contributions to HRV, clinical applications, HRV data acquisition, HRV data manipulation and HRV analysis using time-domain, frequency-domain, time-frequency, nonlinear and fractal techniques. Detailed examples based on real data sets are provided throughout the book to illustrate the algorithms and discuss the physiological implications of the results. Offering a comprehensive guide to analyzing beat information with RHRV, the book is intended for masters and Ph.D. students in various disciplines such as biomedical engineering, human and veterinary medicine, biology, and pharmacy, as well as researchers conducting heart rate variability analyses on both human and animal data.

Handbook of Regression Analysis With Applications in R

2020-08-18
 |  Mathematics
Handbook of Regression Analysis With Applications in R

Author: Samprit Chatterjee

Publisher: John Wiley & Sons

ISBN: 9781119392378

Category: Mathematics

Page: 384

View: 821

Get eBOOK →
Handbook and reference guide for students and practitioners of statistical regression-based analyses in R Handbook of Regression Analysis with Applications in R, Second Edition is a comprehensive and up-to-date guide to conducting complex regressions in the R statistical programming language. The authors’ thorough treatment of “classical” regression analysis in the first edition is complemented here by their discussion of more advanced topics including time-to-event survival data and longitudinal and clustered data. The book further pays particular attention to methods that have become prominent in the last few decades as increasingly large data sets have made new techniques and applications possible. These include: Regularization methods Smoothing methods Tree-based methods In the new edition of the Handbook, the data analyst’s toolkit is explored and expanded. Examples are drawn from a wide variety of real-life applications and data sets. All the utilized R code and data are available via an author-maintained website. Of interest to undergraduate and graduate students taking courses in statistics and regression, the Handbook of Regression Analysis will also be invaluable to practicing data scientists and statisticians.

Data Wrangling with R

2016-11-17
 |  Computers
Data Wrangling with R

Author: Bradley C. Boehmke, Ph.D.

Publisher: Springer

ISBN: 9783319455990

Category: Computers

Page: 238

View: 535

Get eBOOK →
This guide for practicing statisticians, data scientists, and R users and programmers will teach the essentials of preprocessing: data leveraging the R programming language to easily and quickly turn noisy data into usable pieces of information. Data wrangling, which is also commonly referred to as data munging, transformation, manipulation, janitor work, etc., can be a painstakingly laborious process. Roughly 80% of data analysis is spent on cleaning and preparing data; however, being a prerequisite to the rest of the data analysis workflow (visualization, analysis, reporting), it is essential that one become fluent and efficient in data wrangling techniques. This book will guide the user through the data wrangling process via a step-by-step tutorial approach and provide a solid foundation for working with data in R. The author's goal is to teach the user how to easily wrangle data in order to spend more time on understanding the content of the data. By the end of the book, the user will have learned: How to work with different types of data such as numerics, characters, regular expressions, factors, and dates The difference between different data structures and how to create, add additional components to, and subset each data structure How to acquire and parse data from locations previously inaccessible How to develop functions and use loop control structures to reduce code redundancy How to use pipe operators to simplify code and make it more readable How to reshape the layout of data and manipulate, summarize, and join data sets

Learn ggplot2 Using Shiny App

2017-04-13
 |  Computers
Learn ggplot2 Using Shiny App

Author: Keon-Woong Moon

Publisher: Springer

ISBN: 9783319530192

Category: Computers

Page: 351

View: 376

Get eBOOK →
This book and app is for practitioners, professionals, researchers, and students who want to learn how to make a plot within the R environment using ggplot2, step-by-step without coding. In widespread use in the statistical communities, R is a free software language and environment for statistical programming and graphics. Many users find R to have a steep learning curve but to be extremely useful once overcome. ggplot2 is an extremely popular package tailored for producing graphics within R but which requires coding and has a steep learning curve itself, and Shiny is an open source R package that provides a web framework for building web applications using R without requiring HTML, CSS, or JavaScript. This manual—"integrating" R, ggplot2, and Shiny—introduces a new Shiny app, Learn ggplot2, that allows users to make plots easily without coding. With the Learn ggplot2 Shiny app, users can make plots using ggplot2 without having to code each step, reducing typos and error messages and allowing users to become familiar with ggplot2 code. The app makes it easy to apply themes, make multiplots (combining several plots into one plot), and download plots as PNG, PDF, or PowerPoint files with editable vector graphics. Users can also make plots on any computer or smart phone. Learn ggplot2 Using Shiny App allows users to Make publication-ready plots in minutes without coding Download plots with desired width, height, and resolution Plot and download plots in png, pdf, and PowerPoint formats, with or without R code and with editable vector graphics