Longitudinal Analysis of Real World Time-To-Event Data in Health Care: Big Data Approach Using R
暫譯: 健康照護中實際事件時間的縱向分析：使用 R 的大數據方法
Bhattacharjee, Atanu
商品描述
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.
商品描述(中文翻譯)
這本書提供了一種實用的方法，幫助研究人員分析患者隨時間變化的數據。它作為一本全面的指南，利用 R 程式語言有效地分析複雜的數據集。書中提供逐步的指導和範例，幫助數據組織和深入分析，以準確預測事件的發生及不同變數對患者結果的影響，從而提升醫療實踐中的決策能力。
- 通過實用的範例和案例研究，幫助學習如何將分析技術應用於現實世界的醫療數據集，獲得對複雜數據的洞察，以便做出明智的決策。
- 提供相關技術和方法的全面覆蓋，包括重要主題，如大數據特徵、實證醫學（Real-World Evidence）的重要性、現實世界數據來源、縱向和生存數據分析、預測模型以及貝葉斯分析。
- R 代碼範例使讀者能夠跟隨並在自己的數據集上重現分析，加強對數據分析的理解和實踐技能。
- 複雜的統計概念被清晰地解釋，理論與實踐的實施達到平衡，以確保對概念和技術的理解。
- 解釋了大數據如何改變醫療和研究，涉及精準醫療、人口健康管理，以及如何用實證醫學（RWE）補充臨床試驗。
本書涵蓋數據預處理、整合和先進建模技術，成為尋求基於證據的醫療決策及相關領域專業人士和研究人員的寶貴資源。
作者簡介
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.
作者簡介(中文翻譯)
Atanu Bhattacharjee 是萊斯特大學的醫學統計學家。他在醫學統計領域具有專業知識，專注於生存分析、競爭風險和高維數據。Bhattacharjee 的研究興趣包括開發新的統計方法來分析事件發生時間數據，特別是針對競爭風險和高維數據的分析。他在這些主題上已在多本領先的統計期刊上發表了幾篇研究論文和文章。Bhattacharjee 也為 R 套件的開發做出了貢獻，該套件可用於分別進行競爭風險分析和高維數據分析。
