Basic Statistics for Clinicians_4. Correlation and Regression.pdf

# Basic Statistics for Clinicians_4. Correlation and Regression.pdf

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[ statistics * statistique I BASIC STATISTICS FOR CLINICIANS: 4. CORRELATION AND REGRESSION Gordon Guyatt,*t MD; Stephen Walter,* PhD; Harry Shannon,* PhD; Deborah Cook,*t MD; Roman Jaeschke,*t MD; Nancy Heddle,t MSc oR' Correlation and regression help us to understand the relation between variables and to predict patients' status in regard to a particular variable of interest. Correlation examines the strength of the relation between two variables, neither of which is considered the variable one is trying to predict (the target variable). Regression analysis examines the ability of one or more factors, called independent variables, to predict a patient's status in regard to the target or dependent vari- able. Independent and dependent variables may be continu- ous (taking a wide range of values) or binary (dichotomous, yielding yes-or-no results). Regression models can be used to construct clinical prediction rules that help to guide clinical decisions. In considering regression and correlation, clini- cians should pay more attention to the magnitude of the cor- relation or the predictive power of the regression than to whether the relation is statistically significant. linicians are sometimes interested in the relation be- tween different factors or "variables." How well does a relative's impression of a patient's symptoms and well-being predict the patient's own report? How strong is the relation between a patient's physical well-being and emotional func- tion? In answering these questions, our goal is to enhance our understanding and consider the implications for action. If the relation between patients' perceptions and those of patients' relatives is not a strong one, the clinician must ob- tain both perspectives on a situation. If physical and emo- tional function are only weakly related, then clinicians must probe both areas thoroughly. Clinicians may be even more interested in making pre- La correlation et la regression aident 'a comprendre le rapport entre des variables et a pr6dire l6tat de patients en fonction d'une variable particuliere d'intrft. La correlation porte sur la force du rapport entre deux variables dont ni l'une ni lautre n'est consid6r6e comme la variable que lIon essaie de predire (la variable cible). L'analyse de regression porte sur la capa- cite dfun ou de plusieurs facteurs, appel6s variables indkpen- dantes, dtaider 'a predire le'tat d'un patient en fonction de la variable cible ou d6pendante. Les variables independantes et dependantes peuvent etre soit continues (prendre tout un eventail de valeurs), soit binaires (etre dichotomiques, c'est-a- dire donner des r6sultats presence-absence). On peut utiliser des mod&les de regression pour construire des regles de pr6- diction cliniques qui aident a guider les d&isions cliniques.

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