Wow! eBook
Longitudinal Analysis of Real World Time-to-event Data in Health Care: Big Data Approach using R
Author(s): Atanu Bhattacharjee (Author)
- Publisher: Chapman and Hall/CRC
- Publication Date: 26 Jun. 2026
- Edition: 1st
- Language: English
- Print length: 228 pages
- ISBN-10: 1032847476
- ISBN-13: 9781032847474
Book Description
This book presents a practical approach for researchers seeking to analyse patient data over time. It serves as a comprehensive guide, utilising the R programming language to analyse complex datasets efficiently. It provides step-by-step instructions and examples, aiding in data organisation and insightful analysis to accurately predict event occurrences and the impact of different variables on patient outcomes, enhancing decision-making in medical practice.
- With practical examples and case studies, it helps to learn how to apply analysis techniques to real-world healthcare datasets, gaining insights into complex data for informed decision-making
- Offers comprehensive coverage of relevant techniques and methodologies, including essential topics such as Big Data characteristics, Real-World Evidence significance, real-world data sources, longitudinal and survival data analysis, prediction models, and Bayesian analysis
- R code examples enable readers to follow along and replicate analyses on their own datasets, reinforcing understanding and practical skills in data analysis
- Complex statistical concepts are explained clearly, and theory and practical implementation are balanced to ensure an understanding of both concepts and techniques
- Explained how Big Data transforms healthcare and research, touching on precision medicine, population health management, and complementing clinical trials with RWE
It covers data preprocessing, integration, and advanced modelling techniques to serve as a valuable resource for professionals and researchers seeking evidence-based decision-making in healthcare and related fields.
Editorial Reviews
About the Author
Atanu Bhattacharjee is a medical statistician the University of Leicester. He is an expert in the field of medical statistics, with a focus on survival analysis, competing risks, and high-dimensional data. Bhattacharjee’s research interests include the development of new statistical methods for the analysis of time-to-event data, with a focus on the analysis of competing risks and high-dimensional data. He has published several research papers and articles in leading statistical journals on these topics. Bhattacharjee has also contributed to the development of R package, which can be used to perform competing risks analysis and high-dimensional data analysis respectively.
{“@context”:”:”https://schema.org”,”@type”:”Book”,”name”:”Longitudinal Analysis of Real World Time-to-event Data in Health Care: Big Data Approach using R”,”image”:”https://m.media-amazon.com/images/I/51sYDeALLRL._SY445_SX342_ML2_.jpg”,”author”:{“@type”:”Person”,”name”:”Atanu Bhattacharjee (Author)”},”publisher”:{“@type”:”Organization”,”name”:”Chapman and Hall/CRC”},”datePublished”:”26 Jun. 2026″,”isbn”:”9781032847474″,”numberOfPages”:228,”inLanguage”:”English”,”description”:”This book presents a practical approach for researchers seeking to analyse patient data over time. It serves as a comprehensive guide, utilising the R programming language to analyse complex datasets efficiently. It provides step-by-step instructions and examples, aiding in data organisation and insightful analysis to accurately predict event occurrences and the impact of different variables on patient outcomes, enhancing decision-making in medical practice.With practical examples and case studies, it helps to learn how to apply analysis techniques to real-world healthcare datasets, gaining insights into complex data for informed decision-makingOffers comprehensive coverage of relevant techniques and methodologies, including essential topics such as Big Data characteristics, Real-World Evidence significance, real-world data sources, longitudinal and survival data analysis, prediction models, and Bayesian analysisR code examples enable readers to follow along and replicate analyses on their own datasets, reinforcing understanding and practical skills in data analysisComplex statistical concepts are explained clearly, and theory and practical implementation are balanced to ensure an understanding of both concepts and techniquesExplained how Big Data transforms healthcare and research, touching on precision medicine, population health management, and complementing clinical trials with RWEIt covers data preprocessing, integration, and advanced modelling techniques to serve as a valuable resource for professionals and researchers seeking evidence-based decision-making in healthcare and related fields.”,”bookEdition”:”1st”,”url”:”https://www.amazon.co.uk/dp/1032847476/”,”bookFormat”:”http://schema.org/EBook”,”additionalType”:”http://schema.org/PDF”,”fileSize”:”76 MB”,”accessibilityFeature”:[“login required”,”member access only”],”accessibilitySummary”:”PDF version available to authenticated members only. File size: 76 MB.”}
