Michael J. Daniels - Böcker

Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. Unlike parametric methods, the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest.The book is divided into three parts. Part I develops the key concepts in causal inference and missing data and reviews relevant concepts in Bayesian inference. Part II introduces the fundamental BNP tools required to address causal inference and missing data problems. Part III shows how the BNP approach can be applied in a variety of case studies. The datasets in the case studies come from electronic health records data, survey data, cohort studies, and randomized clinical trials.Features• Thorough discussion of both BNP and its interplay with causal inference and missing data• How to use BNP and g-computation for causal inference and non-ignorable missingness• How to derive and calibrate sensitivity parameters to assess sensitivity to deviations from uncheckable causal and/or missingness assumptions• Detailed case studies illustrating the application of BNP methods to causal inference and missing data• R code and/or packages to implement BNP in causal inference and missing data problemsThe book is primarily aimed at researchers and graduate students from statistics and biostatistics. It will also serve as a useful practical reference for mathematically sophisticated epidemiologists and medical researchers.

Several fundamental advances were announced at the Seventh International Symposium on Molecular Plant-Microbe Interactions held in Edinburgh in 1994. These included the cloning and identification of plant resistance genes involved in recognition of pathogens; the description of genetically engineered plants with novel resistance to pathogens; characterization of the molecular basis of pathogenicity of fungal and bacterial plant pathogens; and the mechanisms of communication used during recognition between symbiotic rhizobia and their host legumes. Participants in the Symposium contributed a series of papers that represent the leading edge of research in this important area of plant and microbial science. These articles are brought together to form this book, which should be useful reading for research workers, advanced students and others interested in keeping abreast of this rapidly developing area.

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.