000 04448nam a22005295i 4500
001 978-1-4419-1213-8
003 DE-He213
005 20140220084504.0
007 cr nn 008mamaa
008 100301s2010 xxu| s |||| 0|eng d
020 _a9781441912138
_9978-1-4419-1213-8
024 7 _a10.1007/978-1-4419-1213-8
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aRosenbaum, Paul R.
_eauthor.
245 1 0 _aDesign of Observational Studies
_h[electronic resource] /
_cby Paul R. Rosenbaum.
264 1 _aNew York, NY :
_bSpringer New York,
_c2010.
300 _bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Series in Statistics,
_x0172-7397
505 0 _aBeginnings -- 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.
520 _aAn 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.
650 0 _aStatistics.
650 0 _aStatistical methods.
650 0 _aMathematical statistics.
650 0 _aEconometrics.
650 0 _aSocial sciences
_xMethodology.
650 0 _aPsychological tests and testing.
650 1 4 _aStatistics.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aEconometrics.
650 2 4 _aBiostatistics.
650 2 4 _aPsychological Methods/Evaluation.
650 2 4 _aMethodology of the Social Sciences.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441912121
830 0 _aSpringer Series in Statistics,
_x0172-7397
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-1213-8
912 _aZDB-2-SMA
999 _c110344
_d110344