Data analysis was based on the conjoint analysis technique. The attributes that participants took into consideration and assigned relative importance were the following: severity of disease, age, type of disease, health improvement after treatment, cost of treatment and healthy lifestyle. These six (6) attributes were used as discrete variables. Sixteen (16) preference cards were created based on the six (6) abovementioned attributes and participants ranked order those cards in terms of utility (starting with the most preferred hypothetical patient to the least important hypothetical patient). Using conjoint analysis the relative utility of each of the six (6) attributes was elicited along with their relevant values with highest values indicating high utility and high relative value. Utility (Χ) for each patient profile is derived from the following equation :\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\rm X} \, = {\text{ the model's constant }} + \, {\rm X}_{\text{age}} + \, {\rm X}_{\text{healty lifestyle}} + \, {\rm X}_{\text{type of disease}} + \, {\rm X}_{\text{severity of disease}} + \, {\rm X}_{\text{health improvement}} + \, {\rm X}_{\text{cost of treatment}} .$$\end{document}X=the model's constant+Xage+Xhealty lifestyle+Xtype of disease+Xseverity of disease+Xhealth improvement+Xcost of treatment.