株式会社極東書店トップ ＞ 商品一覧 ＞ Advanced Statistical Analytics for Health Data Science with SAS and R.
商品詳細
Advanced Statistical Analytics for Health Data Science with SAS and R.
・ISBN 978-1-032-97849-9 hard GB£ 103.99
￥32,943.- (税込) ※（※）価格はご注文時の参考価格となります。
納品価格につきましては書籍の入荷時点で確定となります。
版元の原価改定、外国為替の変動等により異なる場合がございますので、予めご了承下さい。
電子版あり 大学・学術機関向け電子ブック（eBook）ISBN 978-1-003-59637-0
| 著者・編者 | Chen, Ding-Geng (Din) / Wilson, Jeffrey, |
|---|---|
| シリーズ | Chapman & Hall/CRC Biostatistics Series |
| 出版社 | (Chapman & Hall/CRC, UK) |
| 出版年月 | 2025 |
| ページ数 | 248 pp. |
| 言語 | ENG |
| ニュース番号 | ＜M25-15048＞ |
解説
In recent years, there has been a growing emphasis on making statistical methods and analytics accessible to health data science researchers and students. Following the first book on "Statistical Analytics for Health Data Science with SAS and R" (2023, www.routledge.com/9781032325620), this book serves as a comprehensive reference for health data scientists, bridging fundamental statistical principles with advanced analytical techniques. By providing clear explanations of statistical theory and its application to real- world health data, we aim to equip researchers with the necessary tools to navigate the evolving landscape of health data science.
Designed for advanced-level data scientists, this book covers a wide range of statistical methodologies, including models for longitudinal data with time-dependent covariates, multi-membership mixed-effects models, statistical modeling of survival data, Bayesian statistics, joint modeling of longitudinal and survival data, nonlinear regression, statistical meta-analysis, spatial statistics, structural equation modeling, latent growth curve modeling, causal inference, and propensity score analysis.
A key feature of this book is its emphasis on real-world applications. We integrate publicly available health datasets and provide case studies from a variety of health applications. These practical examples demonstrate how statistical methods can be applied to solve critical problems in health science.
To support hands-on learning, we offer implementation guidance using SAS and R, ensuring that readers can replicate analyses and apply statistical techniques to their own research. Step-by-step computational examples facilitate reproducibility and deeper exploration of statistical models. By combining theoretical foundations with practical applications, this book empowers health data scientists to develop robust statistical solutions for complex health challenges. Whether working in academia, industry, or public health, readers will gain the expertise to advance data-driven decision-making and contribute to evidence-based health research.