HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Full Text (PDF) Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Gareen, I. F. Articles by Gatsonis, C. Search for Related Content PubMed PubMed Citation Articles by Gareen, I. F. Articles by Gatsonis, C.

Primer on Multiple Regression Models for Diagnostic Imaging Research¹

¹ From the Center for Statistical Sciences, Brown University, Box G-H, 167 Angell St, Providence, RI 02912. Received February 19, 2003; revision requested March 25; revision received May 9; accepted May 20. Supported in part by National Cancer Institute grant CA79778. Address correspondence to I.F.G. (e-mail: igareen@stat .brown.edu).

This article provides an introduction to multiple regression analysis and its application in diagnostic imaging research. We begin by examining why multiple regression models are needed in the evaluation of diagnostic imaging technologies. We then examine the broad categories of available models, notably multiple linear regression models for continuous outcomes and logistic regression models for binary outcomes. The purpose of this article is to elucidate the scientific logic, meaning, and interpretation of multiple regression models by using examples from the diagnostic imaging literature.

© RSNA, 2003

Index terms: Statistical analysis