大葉大學圖書館 |

Statistical causal inferences and their applications in public health research[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	519.5
書名/作者:	Statistical causal inferences and their applications in public health research/ edited by Hua He, Pan Wu, Ding-Geng (Din) Chen.
其他作者:	He, Hua.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xv, 321 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Mathematical statistics.
標題:	Statistics.
標題:	Public health.
標題:	Biometry.
標題:	Statistics for Life Sciences, Medicine, Health Sciences.
標題:	Biostatistics.
標題:	Public Health.
ISBN:	9783319412597
ISBN:	9783319412573
內容註:	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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-41259-7

Statistical causal inferences and their applications in public health research[electronic resource] /
Statistical causal inferences and their applications in public health research[electronic resource] /edited by Hua He, Pan Wu, Ding-Geng (Din) Chen. - Cham :Springer International Publishing :2016. - xv, 321 p. :ill., digital ;24 cm. - ICSA book series in statistics,2199-0980. - ICSA book series in statistics..

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 an important reference.

ISBN: 9783319412597

Standard No.: 10.1007/978-3-319-41259-7doiSubjects--Topical Terms:

171875
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Statistical causal inferences and their applications in public health research[electronic resource] /
LDR:03381nmm a2200337 a 4500 001 467655
003 DE-He213
005 20161026162550.0
006 m d
007 cr nn 008maaau
008 170511s2016 gw s 0 eng d
020 $a 9783319412597 $q (electronic bk.)
020 $a 9783319412573 $q (paper)
024 7 $a 10.1007/978-3-319-41259-7 $2 doi
035 $a 978-3-319-41259-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276
072 7 $a PBT $2 bicssc
072 7 $a MBNS $2 bicssc
072 7 $a MED090000 $2 bisacsh
082 0 4 $a 519.5 $2 23
090 $a QA276 $b .S797 2016
245 0 0 $a Statistical causal inferences and their applications in public health research $h [electronic resource] / $c edited by Hua He, Pan Wu, Ding-Geng (Din) Chen.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xv, 321 p. : $b ill., digital ; $c 24 cm.
490 1 $a ICSA book series in statistics, $x 2199-0980
505 0 $a 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.
520 $a 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.
650 0 $a Mathematical statistics. $3 171875
650 0 $a Statistics. $3 145349
650 0 $a Public health. $3 379938
650 0 $a Biometry. $3 337787
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 464109
650 2 4 $a Biostatistics. $3 464330
650 2 4 $a Public Health. $3 381935
700 1 $a He, Hua. $3 673065
700 1 $a Wu, Pan. $3 673066
700 1 $a Chen, Ding-Geng. $3 559509
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
830 0 $a ICSA book series in statistics. $3 634196
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-41259-7
950 $a Mathematics and Statistics (Springer-11649)