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Design of Observational Studies (Springer Series in Statistics) Paperback – 25 Feb 2012

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From the reviews:

“I should begin by noting that the first time I read the book’s title I thought that it would address the topic of classical epidemiological designs like case-control studies, cohort studies, etc. However, I was wrong. Rosenbaum’s book addresses the crucial topic of designing and analyzing empiric non-randomized investigations to prove causal relationships between treatments and outcomes. These types of studies, the observational ones, are prone to present two well-known selection biases: overt biases, i.e., differences in outcomes between treatments may reflect measured pre-treatment differences between groups rather than effects of treatments, and hidden biases, i.e., the same situation but with pre-treatment differences that were not recorded in the study.

To overcome overt biases, applied statisticians usually advocate the use of model-based adjustments and to overcome hidden biases they usually recommend designing a randomized experiment. However, Rosenbaum’s book addresses these two biases with an alternative approach: propensity score matching for overt biases and sensitivity analysis for hidden biases. Practitioners may think that sensitivity analysis means performing several analyses of the same data set; however, this is not the Rosenbaum’s approach.

Overall, the book is written in a clear and concise way, merging theoretical and practical aspects. Small examples are provided to develop the understanding of key issues in parallel with real examples of practical size from both the economics and the biomedicine areas. Moreover, although the book is not intended as a statistical software oriented book, the text includes some code in R and SAS. For example, Chapter 13 is devoted to matching in R.

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. (2006) for further details. Nowadays, it is yet unusual to address hidden biases in observational studies and, therefore, this book is an essential reading for statisticians who want to go a step beyond from the likely naïve sentence: 'We assume no unmeasured confounding in the study’.” (Journal of Biopharmaceutical Statistics , 2011, Issue 1)

“Graduate students and researchers in statistics, biostatistics, econometrics, or academic researchers in statistically oriented fields of psychology and social sciences. … ‘Design of Observational Studies’ talks about statistics. … The book will be suitable for a seminar course for graduate students with previous knowledge of the subject area, or practicing statisticians seeking guidance in design of observational research and a language to discuss the issues. ‘Design of Observational Studies’ is an important book.” (Erkki P. Liski, International Statistical Review, Vol. 78 (1), 2010)

“This is for those who want to improve whatever their basic design is—for those people, this is a very good book. … have several quick, but useful, guides: key elements in a design, solutions to common problems (very useful), a symbol glossary, a listing of acronyms, a glossary of statistical terms, suggested readings for a course on the design of observational studies, and an index. … anyone who is not an expert on the varieties of matching will profit from reading this.” (Richard Goldstein, Technometrics, Vol. 53 (2), May, 2011)

From the Back Cover

An observational study is an empiric investigation of effects caused by treatments when randomized experimentation is unethical or infeasible. Observational studies are common in most fields that study the effects of treatments on people, including medicine, economics, epidemiology, education, psychology, political science and sociology. The quality and strength of evidence provided by an observational study is determined largely by its design. 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.

Design of Observational Studies is divided into four parts. Chapters 2, 3, and 5 of Part I cover concisely, in about one hundred pages, many of the ideas discussed in Rosenbaum’s Observational Studies (also published by Springer) but in a less technical fashion. Part II discusses the practical aspects of using propensity scores and other tools to create a matched comparison that balances many covariates. Part II includes a chapter on matching in R. 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. 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."

Paul R. Rosenbaum is the Robert G. Putzel Professor of Statistics at the Wharton School of the University of Pennsylvania. He is a fellow of the American Statistical Association. In 2003, he received the George W. Snedecor Award from the Committee of Presidents of Statistical Societies. He is a senior fellow of the Leonard Davis Institute of Health Economics and a Research Associate at the Population Studies Center, both at the University of Pennsylvania. The second edition of his book, Observational Studies, was published by Springer in 2002.

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Amazon.com: 2 reviews
Great book, but expensive to assign 30 Oct 2013
By Sarah Schwartz - Published on Amazon.com
Format: Hardcover
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 Matter and Shadish/Cook/Campbell both take a very strict approach to causal inference, and say that randomized experiments or very specific natural experiments are the only hope for causal inference. While that might make sense in their fields, that's not a realistic approach in fields that are forced to rely on more observational data, and Rosenbaum takes a more realistic approach that allows more observational data.

My one hesitation about assigning the book is cost. I selected Rosenbaum's book initially because I could find many used copies for about 70 each. Now the price for the used copies has increased to 100 and the cover price is 130, meaning that students could buy 3 books for the same cost as this one book: Gelman/Hill (chapters 9-10), Mostly Harmless Econometrics, and Morgan/Winship. This book is better than each of those books for my purposes, but I think that those 3 books together are better than this one alone. It is sad to have such a disparity in price. On the other hand, we are lucky to have 3 good books in causal inference that are so reasonably priced.
2 of 4 people found the following review helpful
Learn from an expert 11 Nov 2012
By scientista - Published on Amazon.com
Format: Hardcover
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.
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