Regression models: calculating the confidence interval of effects in the presence of interactions.
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.
Figueiras
A
A
Department of Preventive Medicine and Public Health, University of Santiago, Spain.
Domenech-Massons
J M
JM
Cadarso
C
C
D000328:Adult; D000367:Age Factors; D001794:Blood Pressure; D003198:Computer Simulation; D016001:Confidence Intervals; D003276:Contraceptives, Oral / Q000009:adverse effects; D005260:Female; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D016016:Proportional Hazards Models; D012044:Regression Analysis; D012307:Risk Factors; D012907:Smoking; D017673:Sodium Chloride, Dietary; D012984:Software; D013923:Thromboembolism / Q000139:chemically induced
D003276:Contraceptives, Oral; D017673:Sodium Chloride, Dietary