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22대 대한민국 국회 국회정보길라잡이 국회의원검색
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Contents

Preface ix

1 Description of Motivating Examples 1

2 Regression Models 15

3 Methods of Bayesian Inference 39

4 Worked Examples using Complete Data 72

5 Missing Data Mechanisms and Longitudinal Data 85

6 Inference about Full-Data Parameters under Ignorability 115

7 Case Studies: Ignorable Missingness 145

8 Models for Handling Nonignorable Missingness 165

9 Informative Priors and Sensitivity Analysis 216

10 Case Studies: Nonignorable Missingness 233

Distributions 268

Bibliography 271

Author Index 292

Index 298

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Missing data in longitudinal studies : strategies for Bayesian modeling and sensitivity analysis 이용현황 표 - 등록번호, 청구기호, 권별정보, 자료실, 이용여부로 구성 되어있습니다.
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Drawing from the authors’ own work and from the most recent developments in the field, Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis describes a comprehensive Bayesian approach for drawing inference from incomplete data in longitudinal studies. To illustrate these methods, the authors employ several data sets throughout that cover a range of study designs, variable types, and missing data issues.

The book first reviews modern approaches to formulate and interpret regression models for longitudinal data. It then discusses key ideas in Bayesian inference, including specifying prior distributions, computing posterior distribution, and assessing model fit. The book carefully describes the assumptions needed to make inferences about a full-data distribution from incompletely observed data. For settings with ignorable dropout, it emphasizes the importance of covariance models for inference about the mean while for nonignorable dropout, the book studies a variety of models in detail. It concludes with three case studies that highlight important features of the Bayesian approach for handling nonignorable missingness.

With suggestions for further reading at the end of most chapters as well as many applications to the health sciences, this resource offers a unified Bayesian approach to handle missing data in longitudinal studies.

This book provides a unified Bayesian approach to handle missing data in longitudinal studies. It contains examples and case studies on schizophrenia, aging, HIV, and smoking cessation. The authors describe assumptions that include MAR and ignorability, demonstrate the importance of covariance modeling with incomplete data, and cover mixture and selection models for nonignorable missingness. They also present methods for representing untestable assumptions using prior distributions. Several analyses deal with nonignorable missingness as well as illustrate the models and methods. Many analyses are implemented using WinBUGS, with the code provided on a supplementary web page.

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