Bergamaschi R, Romani A, Tonietti S, Citterio A, Berzuini C, Cosi V
Neurological Institute Fondazione C. Mondino, University of Pavia, Italy.
Neurol Sci. 2000;21(4 Suppl 2):S819-23. doi: 10.1007/s100720070019.
Previous studies of possible prognostic indicators for multiple sclerosis have been based on "classic" Cox's proportional hazards regression model, as well as on equivalent or simpler approaches, restricting their attention to variables measured either at disease onset or at a few points during follow-up. The aim of our study was to analyse the risk of reaching secondary progression in MS patients with a relapsing-remitting initial course, using two different statistical approaches: a Cox's proportional-hazards model and a Bayesian latent-variable model with Markov chain Monte Carlo methods of computation. In comparison with a standard statistical approach, our model is advantageous because, exploiting all the information gleaned from the patient as it is gradually made available, it is capable to detect even small prognostic effects.
先前关于多发性硬化症可能的预后指标的研究基于“经典”的Cox比例风险回归模型，以及等效或更简单的方法，这些研究将注意力局限于在疾病发作时或随访期间的几个时间点测量的变量。我们研究的目的是使用两种不同的统计方法，分析初始病程为复发缓解型的多发性硬化症患者进入继发进展期的风险：一种是Cox比例风险模型，另一种是采用马尔可夫链蒙特卡罗计算方法的贝叶斯潜变量模型。与标准统计方法相比，我们的模型具有优势，因为它利用从患者那里逐渐获得的所有信息，甚至能够检测到微小的预后影响。
