Regression Methods in Biostatistics Linear, Logistic, Survival, and
This new book 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 surv
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Eric Vittinghoff David V. Glidden
Stephen C. Shiboski Charles E. McCulloch
Regression Methods in Biostatistics Linear, Logistic, Survival, and Repeated Measures Models With 54 Illustrations
Eric Vittinghoff Department of Epidemiology and Biostatistics University of California San Francisco, CA 94143 USA [email protected]
David V. Glidden Department of Epidemiology and Biostatistics University of California San Francisco, CA 94143 USA [email protected]
Stephen C. Shiboski Department of Epidemiology and Biostatistics University of California San Francisco, CA 94143 USA [email protected]
Charles E. McCulloch Department of Epidemiology and Biostatistics University of California San Francisco, CA 94143 USA [email protected]
Series Editors M. Gail National Cancer Institute Rockville, MD 20892 USA
K. Krickeberg Le Chatelet F-63270 Manglieu France
A. Tsiatis Department of Statistics North Carolina State University Raleigh, NC 27695 USA
W. Wong Department of Statistics Stanford University Stanford, CA 94305-4065 USA
J. Samet Department of Epidemiology School of Public Health Johns Hopkins University 615 Wolfe Street Baltimore, MD 21205-2103 USA
Library of Congress Cataloging-in-Publication Data Regression methods in biostatistics : linear, logistic, survival, and repeated measures models / Eric Vittinghoff ... [et al.]. p. cm. — (Statistics for biology and health) Includes bibliographical references and index. ISBN 0-387-20275-7 (alk. paper) 1. Medicine—Research—Statistical methods. 2. Regression analysis. 3. Biometry. I. Vittinghoff, Eric. II. Series. R853.S7R44 2004 610′.72′7—dc22 2004056545 ISBN 0-387-20275-7
Printed on acid-free paper.
© 2005 Springer Science+Business Media, Inc. 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, Inc., 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 in the United States of America. 9 8 7 6 5 4 3 2 1 springeronline.com
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SPIN 10946190
For Jessie & Dannie, E.J., Caroline, Erik & Hugo, and J.R.
Preface
The primary biostatistical tools in modern medical research are single-outcome, multiple-predictor methods: multiple linear regression for continuous outcomes, logistic regression for binary outcomes, and the Cox proportional hazards model for time-to-event outcomes. More recently, generalized linear models and regression methods for repeated outcomes have come into widespread use in the medical research literature. Applying th
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