Krongrad A, Granville L J, Burke M A, Golden R M, Lai S, Cho L, Niederberger C S
Department of Urology, University of Miami School of Medicine, Florida, USA.
J Urol. 1997 Feb;157(2):534-8.
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.
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.
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.
Our observations suggested that efforts to optimize general quality of life in patients with prostate disease must integrate disease nonspecific variables.
针对特定疾病人群的研究主要强调疾病特异性生活质量，而对总体生活质量的研究较少。此外，针对特定疾病人群总体生活质量的研究大多考察了疾病特异性变量的重要性，且这些变量与总体生活质量的相关性普遍较差。我们试图构建前列腺疾病患者总体生活质量的情感成分模型。
我们前瞻性地整合了疾病特异性和非特异性的临床及患者自我报告数据。我们还应用神经网络和更传统的统计工具，以检验各种可用分析方法在构建总体生活质量模型中的相对用途。
神经网络构建了总体生活质量情感成分的合理良好模型。逻辑回归分析也构建了合理良好的模型，并且在当前计算方案下，与前馈反向传播神经网络相比，更易于识别模型中的显著输入变量。所有总体生活质量模型主要依赖于疾病非特异性输入变量，包括社会支持、日常生活活动和应对方式。
我们的观察结果表明，优化前列腺疾病患者总体生活质量的努力必须整合疾病非特异性变量。
