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Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives
This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.
Index-catalogue of Medical and Veterinary Zoology
Supplements 1-14 have Authors sections only; supplements 15-24 include an additional section: Parasite-subject catalogue.
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Integration of NGS in clinical and public health microbiology workflows: applications, compliance, quality considerations
The expansion of NGS implementation in clinical and public health practice accelerated drastically during the SARS-CoV-2 pandemic, where NGS has been playing a vital role in tracking dangerous strains of the virus. NGS applications not only influenced public health decision-making but also have been crossing into the clinical field with individual patients’ results being potentially available to the physicians. Hence, the topic of implementation of NGS methods in clinical and public health microbiology, its challenges and special considerations, is as timely as ever. The use of Next Generation Sequencing (NGS) in clinical and public health microbiology laboratories has been steadily expanding in the past decade. However, this progress has been held back by multiple logistical challenges, like the absence of regulatory compliance framework, lack of clear quality guidelines, the need for standardization and interoperability between laboratories, as well as cost and turn-around-time limitations.
