Introduction: When choosing the best treatment for a given patient, a physician must compare the patient's profile with each treatment's target population. A treatment target population needs to be defined using variables that interact with the size of the treatment effect. These variables include the patients' characteristics, treatment and dose, and also compliance among others. For each set of values for these variables the quantity of treatment effect will be different. The structure of the target population can be represented simply using multidimensional graphical techniques, and this approach may help to determine the optimal treatment-patient association.

Objective: To develop a graphical tool to represent the evolution of the quantity of treatment effect in a space of variables that interact with all the possible treatments corresponding with the therapeutic objective identified. The limitations we set were that the graphical representation(s) chosen had to be, reproducible, simple to use and readily understandable by physicians without particular training.

Methods: We reviewed the available multidimensional graphical methods which have no limits for the number of variables (both qualitative and quantitative), and these methods were used to represent the target populations.

Results: Among these techniques, both linear and polar profiles offer the advantages of allowing the quantity of treatment effect to be visualised as a function of the variables that interact with the treatment effect. The target population can also be visualised, a given patient can be positioned in the space of the target population, and the expected treatment effect for a given patient can be expressed. Furthermore, those techniques satisfy the requirements of reproducibility and simplicity of use for untrained physicians.

Discussion: This approach should be investigated further to evaluate its acceptability to a larger sample of physicians.