Statistical causal inferences and their applications in public health research
著者
書誌事項
Statistical causal inferences and their applications in public health research
(ICSA book series in statistics / series editors, Jiahua Chen, Ding-Geng (Din) Chen)
Springer, c2016
大学図書館所蔵 全2件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references
内容説明・目次
内容説明
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 an important reference.
目次
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.
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