TY  - BOOK
AU  - Rosenbaum,Paul R.
ED  - SpringerLink (Online service)
TI  - Design of Observational Studies
T2  - Springer Series in Statistics,
SN  - 9781441912138
AV  - QA276-280 
U1  - 519.5 23
PY  - 2010///
CY  - New York, NY
PB  - Springer New York
KW  - Statistics
KW  - Statistical methods
KW  - Mathematical statistics
KW  - Econometrics
KW  - Social sciences
KW  - Methodology
KW  - Psychological tests and testing
KW  - Statistical Theory and Methods
KW  - Biostatistics
KW  - Psychological Methods/Evaluation
KW  - Methodology of the Social Sciences
N1  - Beginnings -- Dilemmas and Craftsmanship -- Causal Inference in Randomized Experiments -- Two Simple Models for Observational Studies -- Competing Theories Structure Design -- Opportunities, Devices, and Instruments -- Transparency -- Matching -- A Matched Observational Study -- Basic Tools of Multivariate Matching -- Various Practical Issues in Matching -- Fine Balance -- Matching Without Groups -- Risk-Set Matching -- Matching in R -- Design Sensitivity -- The Power of a Sensitivity Analysis and Its Limit -- Heterogeneity and Causality -- Uncommon but Dramatic Responses to Treatment -- Anticipated Patterns of Response -- Planning Analysis -- After Matching, Before Analysis -- Planning the Analysis
N2  - 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
UR  - http://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-1-4419-1213-8
ER  -