Statistical Causal Inferences and Their Applications in Public Health Research / edited by Hua He, Pan Wu, Ding-Geng (Din) Chen.

Format:

Book

Contributor:

He, Hua., Editor.

Wu, Pan., Editor.

Chen, Ding-Geng (Din)., Editor.

Series:

ICSA Book Series in Statistics, 2199-0980

Language:

English

Subjects (All):

Statistics.

Biometry.

Public health.

Statistics for Life Sciences, Medicine, Health Sciences.

Biostatistics.

Public Health.

Local Subjects:

Statistics for Life Sciences, Medicine, Health Sciences.

Biostatistics.

Public Health.

Physical Description:

1 online resource (XV, 321 p. 24 illus., 11 illus. in color.)

Edition:

1st ed. 2016.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2016.

Summary:

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.

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. .

Notes:

Includes bibliographical references at the end of each chapters and index.