<電子ブック>
Statistical Causal Inferences and Their Applications in Public Health Research / edited by Hua He, Pan Wu, Ding-Geng (Din) Chen
(ICSA Book Series in Statistics. ISSN:21990999)
版 | 1st ed. 2016. |
---|---|
出版者 | Cham : Springer International Publishing : Imprint: Springer |
出版年 | 2016 |
本文言語 | 英語 |
大きさ | XV, 321 p. 24 illus., 11 illus. in color : online resource |
著者標目 | He, Hua editor Wu, Pan editor Chen, Ding-Geng (Din) editor SpringerLink (Online service) |
件 名 | LCSH:Biometry LCSH:Public health FREE:Biostatistics FREE:Public Health |
一般注記 | Part I. Overview -- 1. Causal Inference – A Statistical Paradigm for Inferring Causality -- Part II. Propensity Score Method for Causal Inference -- 2. Overview of Propensity Score Methods -- 3. Sufficient Covariate, Propensity Variable and Doubly Robust Estimation -- 4. A Robustness Index of Propensity Score Estimation to Uncontrolled Confounders -- 5. Missing Confounder Data in Propensity Score Methods for Causal Inference -- 6. Propensity Score Modeling & Evaluation -- 7. Overcoming the Computing Barriers in Statistical Causal Inference -- Part III. Causal Inference in Randomized Clinical Studies -- 8. Semiparametric Theory and Empirical Processes in Causal Inference -- 9. Structural Nested Models for Cluster-Randomized Trials -- 10. Causal Models for Randomized Trials with Continuous Compliance -- 11. Causal Ensembles for Evaluating the Effect of Delayed Switch to Second-line Antiretroviral Regimens -- 12. Structural Functional Response Models for Complex Intervention Trials -- Part IV. Structural Equation Models for Mediation Analysis -- 13.Identification of Causal Mediation Models with An Unobserved Pre-treatment Confounder -- 14. A Comparison of Potential Outcome Approaches for Assessing Causal Mediation -- 15. Causal Mediation Analysis Using Structure Equation Models. This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in Statistics, Biostatistics and Computational Biology. Researchers and data analysts in public health and biomedical research will also find this book to be animportant reference HTTP:URL=https://doi.org/10.1007/978-3-319-41259-7 |
目次/あらすじ
所蔵情報を非表示
電子ブック | 配架場所 | 資料種別 | 巻 次 | 請求記号 | 状 態 | 予約 | コメント | ISBN | 刷 年 | 利用注記 | 指定図書 | 登録番号 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
電子ブック | オンライン | 電子ブック |
|
|
Springer eBooks | 9783319412597 |
|
電子リソース |
|
EB00238787 |
類似資料
この資料の利用統計
このページへのアクセス回数:3回
※2017年9月4日以降