rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Statistical Causal Inferences and Their Applications in Public Health Research
  name:
    name part: He, Hua.
    role:
      role term: editor.
  name:
    name part: Wu, Pan.
    role:
      role term: editor.
  name:
    name part: Chen, Ding-Geng (Din).
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XV, 321 p. 24 illus., 11 illus. in color. online resource.
  abstract: 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.
  table of contents: 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. .
  note: edited by Hua He, Pan Wu, Ding-Geng (Din) Chen.
  subject:
    topic: Statistics
  subject:
    topic: Public health
  subject:
    topic: Biostatistics
  subject:
    topic: Statistics
  subject:
    topic: Statistics for Life Sciences, Medicine, Health Sciences
  subject:
    topic: Biostatistics
  subject:
    topic: Public Health
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: ICSA Book Series in Statistics
  identifier: 9783319412597
  identifier: http://dx.doi.org/10.1007/978-3-319-41259-7
  record info:
    record creation date: 161026
    record change date: 20161026162550.0
    record identifier: 978-3-319-41259-7