Predictors of General Quality of Life in Patients With Benign Prostate Hyperplasia or Prostate Cancer

PURPOSE: Studies in disease specific populations have emphasized disease specific quality of life with little study of general quality of life. Furthermore, studies of general quality of life in disease specific populations have mostly examined the importance of disease specific variables, and have generally yielded poor correlations of such variables and general quality of life. We attempted to model the emotional component of general quality of life in patients with prostate disease. MATERIALS AND METHODS: We integrated prospectively collected disease specific and nonspecific clinical and self-reported patient data. We also applied neural network and more conventional statistical tools to examine the relative use of various available analytical methodologies in modeling general quality of life. RESULTS: Neural networks created reasonably good models of the emotional component of general quality of life. Logistic regression analysis also created reasonably good models and, given current computational schemes, allowed for identification of significant inputs in the models more readily than did the feed-forward, back propagation neural networks. All models of general quality of life relied primarily on disease nonspecific inputs, including social support, activities of daily living and coping. CONCLUSIONS: Our observations suggested that efforts to optimize general quality of life in patients with prostate disease must integrate disease nonspecific variables.