The integrated patient-centered, evidence-based approach to care recognizes the role of patient preferences. A discrete choice experiment (DCE) was developed with the aim of identifying the preferences of men with prostate cancer in Italy regarding the different risk-benefit factors of various treatment options.

The DCE was developed with the support of prostate cancer patients and oncologists and was based on a targeted scoping review. The final DCE included 26 choice sets divided into two blocks. The first block focused on all prostate cancer patients (both metastatic and non-metastatic), while the second block aimed to assess preferences for patients with metastatic hormone-sensitive prostate cancer (mHSPC). Patients were asked to choose from ten attributes in the first block and six in the second block. The aim was to identify attributes and levels with a statistically significant impact on patient preferences. Preference estimates were calculated using a conditional logit regression model and the results were stratified by cancer stage (metastatic or non-metastatic) in the first block.

A total of 202 patients (mean age 72 years) completed the DCE. In the first block, the most important attribute was quality of life (QoL), particularly for patients with metastatic cancer. The other three attributes found to be significant, in order of relevance to patients, were the risks of experiencing cognitive impairment, hematologic complications, and fatigue. For patients with mHSPC, QoL was the strongest determinant of preference. The risk of experiencing fatigue was also a relevant attribute, followed by skin irritation.

This study shows that the effect of treatment on QoL was the most important attribute for patients diagnosed with prostate cancer. Specific risk factors play a different role in the choice of treatment depending on cancer type, with the risk of experiencing fatigue being valued by all groups. Identifying and understanding patients’ preferences related to treatments for prostate cancer will help physicians identify the best treatment strategy.