Longitudinal Analysis of Real World Time-to-event Data in Health Care
Chapman & Hall/CRC (Verlag)
978-1-032-84747-4 (ISBN)
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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.
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.
1. Big Data, Real-World Evidence, and R. 2. Preparing and Exploring Real-World Longitudinal Data in R. 3. Survival Analysis in Real World Evidence Data. 4. Longitudinal Data Analysis in Real-World Evidence. 5. Longitudinal Analysis in Real World Evidence Data. 6. Landmark Data Analysis in Real-World Evidence. 7. Joint Longitudinal and Survival Analysis in Real-World Evidence. 8. Prediction Models with Longitudinal Data. 9. Bayesian Analysis of Big Longitudinal Data.
| Erscheint lt. Verlag | 9.6.2026 |
|---|---|
| Zusatzinfo | 23 Line drawings, black and white; 23 Illustrations, black and white |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra |
| Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie | |
| ISBN-10 | 1-032-84747-6 / 1032847476 |
| ISBN-13 | 978-1-032-84747-4 / 9781032847474 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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