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Regression models: calculating the confidence interval of effects in the presence of interactions

✍ Scribed by Adolfo Figueiras; Jose Maria Domenech-Massons; Carmen Cadarso

Publisher
John Wiley and Sons
Year
1998
Tongue
English
Weight
90 KB
Volume
17
Category
Article
ISSN
0277-6715

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✦ Synopsis

The main goal of regression analysis (multiple, logistic, Cox) is to assess the relationship of one or more exposure variables to a response variable, in the presence of confounding and interaction. The confidence interval for the regression coefficient of the exposure variable, obtained through the use of a computer statistical package, quantify these relationships for models without interaction. Relationships between variables that present interactions are represented by two or more terms, and the corresponding confidence intervals can be calculated 'manually' from the covariance matrix. This paper suggests an easy procedure for obtaining confidence intervals from any statistical package. This procedure is applicable for modifying variables which are continuous as well as categorical.

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