Algorithm to Compare the Slopes (Regression Coefficients) between the subgroups in Simple/Multiple Regression using PROC REG

Regression analysis is the most common technique used for data analysis in clinical trials. In regression analysis, a regression line is fitted for the response variable (e.g. Viral load at the end of the study) based on few explanatory variables (e.g. Baseline viral load, time since first diagnosis etc.). A regression line Y=a+bX is fitted, where Y is the response variable, X is the explanatory variable, a denotes the intercept and b is the slope (regression coefficient) of the line. The slope indicates the change in the value of Y if X is changed by one unit. Therefore slope is often useful measure of examining the rate of change in variable Y. In clinical trials, comparing slope (rate of change) for two (or more subgroups e.g. Active vs. Placebo) can be the area of interest to assess the effect of medical treatment. SAS® procedure PROC REG does not performs the desired analysis directly but some kind of data manipulation is needed. This paper will discuss the algorithm for comparing the regression coefficients for simple/multiple regression for 2 or more subgroups.