Spiegelhalter D J
Stat Med. 1986 Sep-Oct;5(5):421-33. doi: 10.1002/sim.4780050506.
It is argued that the provision of accurate and useful probabilistic assessments of future events should be a fundamental task for biostatisticians collaborating in clinical or experimental medicine, and we explore two aspects of obtaining and evaluating such predictions. When covariate information on patients is available, logistic regression and other multivariate techniques are often used to select prognostic factors and create predictive models. An example shows how the explicit aim of prediction needs to be taken into account in such modelling, and how predictive performance may be assessed by decomposition of a scoring rule. Secondly, results from a program that provides pretrial and interim predictions in clinical trials are displayed, bringing together the use of subjective opinion, Bayesian methodology and techniques for evaluating and criticizing predictions.
有人认为，为未来事件提供准确且有用的概率评估应该是从事临床或实验医学的生物统计学家的一项基本任务，并且我们探讨了获取和评估此类预测的两个方面。当可获得患者的协变量信息时，逻辑回归和其他多变量技术通常用于选择预后因素并创建预测模型。一个例子展示了在这种建模中如何考虑预测的明确目标，以及如何通过分解评分规则来评估预测性能。其次，展示了一个在临床试验中提供审前和中期预测的程序的结果，该程序综合运用了主观意见、贝叶斯方法以及评估和批评预测的技术。
