Design of Observational Studies (Springer Series in Statistics)
|Rating||:||4.72 (507 Votes)|
|Number of Pages||:||384 Pages|
In Part III, the concept of design sensitivity is used to appraise the relative ability of competing designs to distinguish treatment effects from biases due to unmeasured covariates. Design of Observational Studies is divided into four parts. An observational study is an empiric investigation of effects caused by treatments when randomized experimentation is unethical or infeasible. Part II discusses the practical aspects of using propensity scores and other tools to create a matched comparison that balances many covariates. Design of Observational Studies is both an introduction to statistical inference in observational studies and a detailed discussion of the principles that guide the design of observational studies. Part II includes a chapter on matching in R. The quality and strength of evidence provided by an observational study is determined largely by its design. Part IV discusses planning the analysis of an observational study, with particular reference to Sir Ronald Fisher’s striking advice for observational studies, "make your theories elaborate." The second edition of his book, Observational Studies, was published by Springer in 2002.. Chapters 2, 3, and 5 of Part I cover concisely, in about one hundred pages, many of the ideas discussed in Rosenbaum&rsquo
53 (2), May, 2011)Finally, the book covers all the relevant issues in designing and analyzing treatment effects in observational studies, with the exception of observer bias, i.e., the bias present when the assessment of the outcomes are not valid; see Haro et al. The new book is less technical, proving less and discussing more on philosophy, history, and heuristics of sound and creative study design. … The book contains four parts. This book is ideally suited to bridge the gap between standard epidemiological textbooks and more advanced books such as Observational Studies (Rosenbaum, 2002)." Journal of the Royal Stat
"Great book, but expensive to assign" according to Sarah Schwartz. I did an extensive textbook review for my graduate-level causal inference course. The good news is that all of the books that I reviewed are clear and easy to read. The difference comes in the content of the books and the costs.Rosenbaum is the only book that uses Rubin's causal inference framework, without potentially confusing students with the directed acyclic graphs, unlike Morgan and Winship. Unlike Angrist/Pischke (Mostly Harmless Econometrics) and Willett/Forget (Methods Matter), Design of Observational studies is not specific to a discipline or content area, such as economics and education, respectively. Methods M. Learn from an expert Professor Rosenbaum is a beast (that's a compliment). I don't know why no one else has reviewed this book or why it's not more popular, but it's pretty solid if you're interested in what can be gleaned from observational data. Observational data is different than experimental data. Observational studies seek to understand the effects of interventions when the intervention is not randomized. This topic is important in the health sciences, education, and social sciences because of the ease of obtaining observational data and the problems that can possibly be answered from the data.