Longitudinal Analysis of Real World Time-to-event Data in Health Care
Big data approach using R
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Product details:
- Edition number 1
- Publisher Chapman and Hall
- Date of Publication 26 June 2026
- ISBN 9781032847474
- Binding Hardback
- No. of pages236 pages
- Size 234x156 mm
- Language English
- Illustrations 23 Illustrations, black & white; 23 Line drawings, black & white 700
Categories
Short description:
This book presents a practical approach for researchers seeking to analyse patient data over time, serving as a comprehensive guide utilising the R programming language to analyse complex datasets efficiently. It is a valuable resource for professionals and researchers seeking evidence-based decision-making in healthcare and related fields.
MoreLong 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.
MoreTable of Contents:
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.
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