Primer on multiple regression models for diagnostic imaging research.
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.
Center for Statistical Sciences, Brown University, Box G-H, 167 Angell Street, Providence, RI 02912, USA. igareen@stat.brown.edu
Ilana F
IF
Gareen
Constantine
C
Gatsonis
D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D003952:Diagnostic Imaging; D016015:Logistic Models; D015999:Multivariate Analysis; D012044:Regression Analysis