Abstract

Background: Due to the pressing need to understand the causal and associative factors of depression among prostate cancer (PCa) patients, a comprehensive research protocol for investigating depression in prostate cancer patients is suggested as a way of furthering the collection of data in consistent and informative ways. Methods: A detailed review of a range of predictors of, and buffers against, depression, plus methods of assessing depressive symptomatology and optimum time to collect data were used to develop a model for a comprehensive research protocol. Results: A model protocol is described that includes socioeconomic, genetic, endocrinal, immunological, physiological, psychological, relationship, and socioeconomic pathways to depression. In addition, methods of assessing depressive symptomatology are described, plus comorbidity of anxiety with depression, male depression, and the construct of Individual Burden of Illness for Depression. The need to collect multiple measures over time in order to describe variability in symptoms and the relationships between symptoms and other variables is emphasized. Conclusion: This model protocol of research into depression in prostate cancer patients allows for a comprehensive approach that includes predictors, symptoms, and time for observation. Use of this protocol will enhance the understanding and treatment of depression in PCa patients from a “personalized medicine” perspective.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.