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Propensity Score Analysis FUNDAMENTALS AND DEVELOPMENTS Edited by Wei Pan and Haiyan Bai Published
by The Guilford Press in April 2015 |
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To the contributing authors for their remarkable cooperation and commitment to the advancement of propensity score methodology Contributing Authors Haiyan Bai,
Ph.D., Department of Learning
Sciences and Educational Research, University of Central Florida, haiyan.bai@ucf.edu Lane
F. Burgette, Ph.D.,
RAND Corporation, burgette@rand.org M.
H. Clark, Ph.D., Department of Learning
Sciences and Educational Research, University of Central Florida, m.h.clark@ucf.edu Tom
H. Greene, Ph.D., Division
of Biostatistics, University of Utah, tom.greene@hsc.utah.edu Beth
Ann Griffin, Ph.D.,
RAND Corporation, bethg@rand.org Rolf
H. H. Groenwold, M.D.,
Ph.D., Leiden
University, The Netherlands, r.h.h.groenwold@lumc.nl Debbie L. Hahs-Vaughn, Ph.D.,
Department of Learning
Sciences and Educational Research, University of Central Florida, debbie.hahs-vaughn@ucf.edu Bradley E. Huitema,
Ph.D., Emeritus, Department of Psychology, Western
Michigan University, brad.huitema@wmich.edu
Ben
Kelcey, Ph.D., School
of Education, University of Cincinnati, benjamin.kelcey@uc.edu Olaf H. Klungel, Pharm.D., Ph.D., Division of
Pharmacoepidemiology and Clinical Pharmacology, Utrecht University, The
Netherlands, o.h.klungel@uu.nl Scott
F. Kosten, Ph.D.,
inVentiv Health Clinical, s.kosten@gmail.com Walter
L. Leite, Ph.D.,
Research and Evaluation Methodology Program, University of Florida, walter.leite@coe.ufl.edu Liang
Li, Ph.D.,
Department of Biostatistics, University of Texas MD Anderson Cancer Center, lli15@mdanderson.org Lingling Li,
Ph.D., Department of
Population Medicine, Harvard Medical School, lingling_li@post.harvard.edu Xiaochun Li,
Ph.D., Department of
Biostatistics, Indiana University, xiaochun@iupui.edu Daniel F.
McCaffrey, Ph.D., Educational
Testing Service, dmccaffrey@ets.org Joseph
W. McKean, Ph.D.,
Department of Statistics, Western Michigan University, joseph.mckean@wmich.edu Robin Mitra,
Ph.D., Mathematical
Sciences, University of Southampton, UK, R.Mitra@soton.ac.uk Wei
Pan, Ph.D., School
of Nursing, Duke University, wei.pan@duke.edu Cassandra W.
Pattanayak, Ph.D.,
Quantitative Analysis Institute, Wellesley College, cpattanayak@wellesley.edu Brian
C. Sauer, Ph.D.,
Division of Epidemiology, University of Utah, brian.sauer@utah.edu Megan Schuler, Ph.D., The Methodology Center, Pennsylvania
State University, mss41@psu.edu Changyu Shen,
Ph.D., Department of
Biostatistics, Indiana University, chashen@iupui.edu Christopher
M. Swoboda, Ph.D., School
of Education, University of Cincinnati, christopher.swoboda@uc.edu Qiu Wang, Ph.D., Department of Higher Education, Syracuse
University, wangqiu@syr.edu (Order
the book from The
Guilford Press or Amazon.com) |
Reviews "Pan and
Bai have assembled a comprehensive volume on all aspects of propensity score
methods. Both the user and the statistician will find something to like in
this book. I recommend it. " -William R. Shadish, Ph.D. (1949-2016), Late Distinguished
Professor of Psychology, University of California, Merced "There
is no question that this book will serve as an excellent resource for those
who want to add PSA to their repertoire of analytical methods. The chapters
provide sufficient materials and examples to help both 'green hands' and
seasoned analysts deal with the methodological and practical challenges of
applying PSA in research work." -Xitao Fan, Ph.D., Wei Lun
Professor and Dean, Faculty of Education, The Chinese University of Hong Kong, China "This book
effectively synthesizes general principles of PSA with recent developments
regarding complex issues such as estimation techniques, covariate balance,
weighting, complex datasets, and sensitivity analysis. The discussion of
statistical software and examples of computer code are helpful additions.
This book will be useful to graduate students and applied researchers who are
interested in learning about PSA for the first time or who have some
knowledge and would like to learn about issues and recent developments. I
recommend it as a textbook for graduate-level courses in methods of causal
inference or as a reference for researchers in the social and biomedical
sciences." -Suzanne E. Graham, Ed.D., Associate Professor, Department of Education, University of
New Hampshire "This
book is a go-to guide for designing and analyzing observational data. The
editors have produced a brilliant work that addresses both methodological and
practical issues in propensity score analysis. A 'must read' for all
biostatisticians as well as applied researchers in the social, behavioral,
and health sciences." -Ding-Geng (Din) Chen, Ph.D., Professor, College of Health Solutions, Arizona State
University |
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Questions? Phone: (919) 684-9324 or Email: wei.pan@duke.edu,
haiyan.bai@ucf.edu |
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