Ohno-Machado L
Decision Systems Group, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Comput Biol Med. 1997 Jan;27(1):55-65. doi: 10.1016/s0010-4825(96)00036-4.
Modeling survival of populations and establishing prognoses for individual patients are important activities in the practice of medicine. For patients with diseases that may extend for several years, in particular, accurate assessment of survival probabilities is essential. New methods, such as neural networks, have been used increasingly to model disease progression. Their advantages and disadvantages, when compared to statistical methods such as Cox proportional hazards, have seldom been explored in real-world data. In this study, we compare the performances of a Cox model and a neural network model that are used as prognostic tools for a set of people living with AIDS. We modeled disease progressions for patients who had AIDS (according to the 1993 CDC definition) in a set of 588 patients in California, using data from the ATHOS project. We divided the study population into 10 training and 10 test sets and evaluated the prognostic accuracy of a Cox proportional hazards model and of a neural network model by determining sensitivities, specificities, positive and negative predictive values for an arbitrary threshold (0.5), and the areas under the receiver operating characteristics (ROC) curves that utilized all possible thresholds for intervals of 1 yr following the diagnosis of AIDS. There was no evidence that the Cox model performed better than did the neural network model or vice versa, but the former method had the advantage of providing some insight on which variables were most influential for prognosis. Nevertheless, it is likely that the assumptions required by the Cox model may not be satisfied in all data sets, justifying the use of neural networks in certain cases.
对人群生存情况进行建模并为个体患者建立预后评估是医学实践中的重要活动。对于患有可能持续数年疾病的患者而言，准确评估生存概率至关重要。诸如神经网络等新方法已越来越多地用于对疾病进展进行建模。与Cox比例风险等统计方法相比，它们的优缺点在实际数据中很少得到探讨。在本研究中，我们比较了Cox模型和神经网络模型作为一组艾滋病患者预后工具的性能。我们利用ATHOS项目的数据，对加利福尼亚州588名患有艾滋病（根据1993年美国疾病控制与预防中心的定义）的患者的疾病进展进行建模。我们将研究人群分为10个训练集和10个测试集，并通过确定任意阈值（0.5）下的敏感性、特异性、阳性和阴性预测值，以及利用艾滋病诊断后1年间隔内所有可能阈值的受试者工作特征（ROC）曲线下面积，评估Cox比例风险模型和神经网络模型的预后准确性。没有证据表明Cox模型比神经网络模型表现更好，反之亦然，但前一种方法的优势在于能对哪些变量对预后影响最大提供一些见解。然而，Cox模型所需的假设在所有数据集中可能并不都能满足，这证明在某些情况下使用神经网络是合理的。
