Paul R. Rosenbaum
Design of Observational Studies
Design of Observational Studies
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Design of Observational Studies, Second Edition is an introduction to statistical inference in observational studies and a detailed discussion of the principles that guide the design of observational studies. It is organized into five parts and features updated exploration of causal influence, with four new chapters, a new R package DOS2 designed as a companion, and discussion of several of the latest matching packages for R.
Format: Paperback / softback
Length: 547 pages
Publication date: 14 July 2021
Publisher: Springer Nature Switzerland AG
Observational studies are a vital tool in the field of research, particularly when randomized experimentation is unethical or infeasible. These studies allow researchers to investigate the effects of treatments on people in a real-world setting. The quality and strength of evidence provided by an observational study are determined largely by its design.
Design of Observational Studies, Second Edition, is a comprehensive guide to statistical inference in observational studies and the principles that guide the design of these studies. The book is organized into five parts, each covering different aspects of observational research.
Part I provides an introduction to statistical inference in observational studies, covering topics such as estimation, hypothesis testing, and confidence intervals. Chapters 2, 3, and 5 of this part cover many of the ideas discussed in Rosenbaums Observational Studies, but in a less technical fashion. This section is designed to provide a solid foundation for those new to the field.
Part II discusses the practical aspects of using propensity scores and other tools to create a matched comparison that balances many covariates. This section includes an updated chapter on matching in R, a popular software tool for statistical analysis. Matching is a technique used to reduce bias in observational studies by comparing individuals who are similar in terms of their covariates to those who are treated differently.
Part III introduces the concept of design sensitivity, which is used to appraise the relative ability of competing designs to distinguish treatment effects from biases due to unmeasured covariates. This section discusses the importance of selecting the right design for a particular study and the factors that should be considered when making this decision.
Part IV is new to this edition and discusses evidence factors and the computerized construction of more than one comparison group. This section provides an overview of the methods used to create multiple comparison groups and the advantages and disadvantages of each approach.
Part V discusses planning the analysis of an observational study, with particular reference to Sir Ronald Fishers striking advice for observational studies: make your theories elaborate. This section provides guidance on developing a clear research question, selecting appropriate variables, and designing an effective analysis plan.
In addition to the main text, Design of Observational Studies, Second Edition, features updated exploration of causal influence, with four new chapters. These chapters cover topics such as causal diagrams, mediation analysis, and structural equation modeling, which are important tools for understanding the relationship between variables in observational studies.
The book also includes a new R package, DOS2, designed as a companion for the book. DOS2 provides a set of functions and tools for implementing the methods discussed in the book, making it easier for researchers to apply these techniques to their own data.
Overall, Design of Observational Studies, Second Edition, is a valuable resource for researchers and practitioners in the field of observational studies. The book provides a comprehensive and up-to-date introduction to statistical inference in observational studies, as well as practical guidance on designing and analyzing these studies. With its updated exploration of causal influence and new R package, DOS2, the book is an essential tool for anyone working in this field.
Weight: 872g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030464073
Edition number: 2nd ed. 2020
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