Regression Methods in Biostatistics Linear, Logistic, Survival, and
This new edition provides a unified, in-depth, readable introduction to the multipredictor regression methods most widely used in biostatistics: linear models for continuous outcomes, logistic models for binary outcomes, the Cox model for right-censored s
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Series Editors: Mitchell Gail Klaus Krickeberg Jonathan M. Samet Anastasios Tsiatis Wing Wong
For further volumes: http://www.springer.com/series/2848
Eric Vittinghoff • David V. Glidden Stephen C. Shiboski • Charles E. McCulloch
Regression Methods in Biostatistics Linear, Logistic, Survival, and Repeated Measures Models Second edition
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Eric Vittinghoff Department of Epidemiology and Biostatistics University of California, San Francisco Parnassas Ave. 500 94143 San Francisco California MU-420 West USA
David V. Glidden Department of Epidemiology and Biostatistics University of California, San Francisco Parnassas Ave. 500 94143 San Francisco California MU-420 West USA
Stephen C. Shiboski Department of Epidemiology and Biostatistics University of California, San Francisco Parnassas Ave. 500 94143 San Francisco California MU-420 West USA
Prof. Charles E. McCulloch Department of Epidemiology and Biostatistics University of California, San Francisco Berry 185 94107 San Francisco California Suite 5700 USA
ISSN 1431-8776 ISBN 978-1-4614-1352-3 e-ISBN 978-1-4614-1353-0 DOI 10.1007/978-1-4614-1353-0 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011945441 © Springer Science+Business Media, LLC 2004, 2012 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
For Rupert & Jean; Kay & Minerva; Caroline, Erik & Hugo; and J.R.
Preface
In the second edition of Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models, we have substantially revised and expanded the core chapters of the first edition, and added two new chapters. The first of these, Chap. 9, on strengthening causal inference, introduces potential outcomes, average causal effects, and two primary methods for estimating these effects, what we call potential outcomes estimation and inverse probability weighting. It also covers propensity scores in detail, then more briefly discusses time-dependent exposures, controlled and natural direct effects, instrumental variables, and principal stratification. The second, Chap. 11, on missing data, explains why this is a problem, classifies missingness by mechanism, and discusses the shortcomings of some simple approaches. Its focus is on three primary approaches for dealing with mis
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