INTRODUCTION AND OBJECTIVES: Recent applications of conjoint analysis (CA) in health care showed the technique’s potential for measuring benefits beyond traditional health outcomes, particularly patient preferences (PP). However, further methodological work is needed to generalize its application into clinical research. This abstract discusses the issues addressed during a recent PP research in benign prostatic hyperplasia (BPH) patients. METHODS: Issues were addressed at the different stages of PP design: 1) identify attributes to include in the scenarios; 2) choose attribute levels; 3) select scenarios to present; 4) decide which was the suitable method to elicit patient preferences; and 5) choose the CA. RESULTS: 1- Attributes were identified using patient interviews, literature review and drug profiles (side-effect, daily cost, time to onset). The selection of the most important attributes for patients addressed the question of defining their relevance. Six attributes were selected in our study. 2- Attribute levels were determined to make trade-off possible while remaining realistic. The margin between these two concepts was narrow for the attributes chosen, for example the daily cost of medical treatment ranged from $1 to $1.5. 3- A fractional factorial design was employed to determine the 16 scenarios that were administered to keep the patient burden acceptable (less than 12 minutes). Three sets of scenarios in different orders were administered, as order may affect the results of CA. 4- The method for PP elicitation was discussed in order to find a common design to assess quality of life, PP and clinical criteria within the same study. Auto-administration methods were preferred to interview-administration, to limit clinician burden and because of the sample size: 871 patients. Rating methods were chosen as the simplest approach. 5- Data were analyzed using ordered probit regression techniques. CONCLUSION: When following theoretical and methodological guidelines for assessing PP, researchers need to take into account study design, the size of the sample and the number of outcomes measured within the same study.