Winkel P, Statland B E
University Hospital of Copenhagen, Denmark.
Immunol Ser. 1990;53:27-38.
In this chapter the application of multivariate techniques for the assessment of laboratory tests in cancer patients has been reviewed. We emphasize that the transformation of laboratory test values into just two categories (normal or abnormal) may entail a considerable loss of information. For instance, correlation between two laboratory tests that may be important for differentiating among various clinical categories of patients may disappear when this procedure is used. When only a single set of laboratory results measured in the same specimen is available for a given patient, we must compare these values to those obtained from other patients or healthy subjects to make inferences about the patient on the basis of the laboratory results. Thus, the analysis of the data must be group based. Discriminant analysis, logistic regression analysis, and survival analysis based on Cox's regression model are the techniques most often used in this situation. By contrast, when previous results are available from the same patient we may compare his or her present values to those previously obtained when we want to make inferences about the patient. Our objective is to make a prediction about the time that will elapse until some specified event (death or recurrence of disease) occurs. Two models that have been applied in this situation--the Markov chain and the autoregressive time series model--were reviewed and examples of specific medical applications presented.
在本章中，我们回顾了多元技术在评估癌症患者实验室检查中的应用。我们强调，将实验室检查值仅分为两类（正常或异常）可能会导致大量信息丢失。例如，当采用这种方法时，对于区分不同临床类型患者可能很重要的两项实验室检查之间的相关性可能会消失。当给定患者仅能获得在同一样本中测量的一组实验室结果时，我们必须将这些值与从其他患者或健康受试者获得的值进行比较，以便根据实验室结果对该患者进行推断。因此，数据的分析必须基于群体。判别分析、逻辑回归分析以及基于考克斯回归模型的生存分析是这种情况下最常使用的技术。相比之下，当同一患者有先前的结果时，我们在想要对该患者进行推断时，可以将其当前值与先前获得的值进行比较。我们的目标是预测直到某个特定事件（死亡或疾病复发）发生所经过的时间。本章回顾了在这种情况下应用的两种模型——马尔可夫链和自回归时间序列模型，并给出了具体医学应用的示例。
